Moritz Von Stietencron

Shantanoo Desai

Klaus-Dieter Thoben

Virtualization of Sea Trials for Smart Prototype Testing

Keywords: IoT, Knowledge-based Engineering, Vessel Design, CFD

The design and development of new vessels is a cost and time-intensive effort, which is greatly reliant on expertise and experience. The prototype building and testing are, especially for small producers who do not sell on volume, often at the same time the production of the first vessel. This further increases the need for other means of reliable and accurate prototype experimentation. This paper presents a procedure for the virtualization of sea trials in which the vessel prototypes are tested, thus generating a concise and reliable data model of the trial, which can be used in simulation and other product development tasks.

Introduction

The landscape of European producers of specialized boats, like emergency response and recovery vessels (ERRV), is marked by small and medium enterprises, which manufacture these vessels on a made-to-order basis. With regards to the product design and development stage, this adds further restraints to the fact, that the total volumes of these vessels are rather small (with the German DGzRS currently employing 39 [START_REF]Numbers and Facts[END_REF] and the Norwegian RS 51 [2] small rescue vessels). With these constraints and the fact that these vessels are financed through donations, there is a significant emphasis on the development phase of the vessels as the margin for building separate prototypes is usually not available. At the same time, of course, there are particularly high standards and requirements [START_REF] Hm Coastguard | [END_REF] attached to each order, as a lot is at stake during search and rescue missions.

The majority of manufacturers already utilize computer-based prototype testing and development methods, e.g., through simulations. However, the output of these measures can only be granted a limited amount of credibility as it vastly relies on assumptions, e.g., about the driving conditions and the corresponding vessel behavior. Often these assumptions are complemented with extensive experience of the involved vessel designers and naval engineers. Still, in the pursuit of reducing uncertainty about the products real-world behavior, real-world sea trials are the state of the art means of product experimentation. While the vessel stability is usually rather well understood by the developers and is the main objective of the testing procedures, an increasing focus is given to the effects of the boat's performance on the personnel and the environment [START_REF] Dobbins | Addressing Crash And Repeated Shock Safety Design Requirements of Fast Craft[END_REF].

The extended usage of the digital development aids is seen as a necessary direction in the production of these vessels but currently faces some challenges which obstruct the implementation of entirely virtual sea trials, of which the reliability of the underlying information is a crucial factor.

This paper presents an approach, to eliminate some of the uncertainty in the process of running virtual sea trials by presenting an approach to digitize real-world sea trials and offer a pathway to the usage of virtual sea trials in a fact-based vessel design process.

Related Work

Across the range of usage of small vessels, the usage patterns vary significantly, which results in a high level of ambiguity in the design process, which this paper proposes to reduce by introducing a higher level of real-world product behavior into the development process. This chapter briefly introduces the two main concepts behind this, namely knowledge-based vessel development and marine sensor data acquisition.

Knowledge-Based Vessel Development

It is necessary to compress development cycles [START_REF] Røstad | ECO-boat MOL capturing data from real use of the product[END_REF] to optimize the vessel development process towards high-quality products without compromising the financial feasibility of the developments. In recent years, concepts like concurrent engineering [START_REF] Jo | Principles of concurrent engineering[END_REF] have been applied to the domain of vessel development [START_REF] Henriksen | Development Projects in SMEs[END_REF]. These advances are complemented by investigations on the integration of product behavior knowledge into a product development process [START_REF] Klein | A proposal for knowledge formalization in product development processes[END_REF][START_REF] Lützenberger | Improving Product-Service Systems by Exploiting Information From The Usage Phase. A Case Study[END_REF]. Together this has led to the implementation of a modeling language for knowledge-based engineering tasks called KbeML (knowledgebased engineering modeling language) in the vessel design process [START_REF] Sullivan | A prospective Data-Oriented Framework for New Vessel Design[END_REF]. This process is highly dependent on a reliable stream of vessel-related sensor data.

Sensor Data Acquisition

Modern vessels contain a large number of sensors within them, depending upon the requirements of the users as well as the vessel designers. These sensors tend to communicate with the vessel's subsystem through standardized protocols developed by the National Marine Electronics Association (NMEA). Two of the well-known standards within the maritime sector are NMEA0183 and NMEA2000. NMEA0183 uses the RS422 Serial interface, while the NMEA2000 uses the modern Computer Area Network (CAN) interface and provides higher speeds compared to NMEA0183 [START_REF] Kim | A Study of Marine Network NMEA2000 for e-Navigation[END_REF].

For modern IoT applications, these protocols produce crucial challenges. Since the data is only available on the local network of the vessel and for simple applications, obtaining information from these networks and sending them to cloud-based infrastruc-ture pose as a critical challenge. Since operating vessels are more often at sea, the information exchange between the vessel and the cloud services becomes a vital task to execute. While there are several commercial systems available to monitor either variables at a global level on the vessel or highly specialized data sets for single development questions [START_REF] Swartz | Hybrid wireless hull monitoring system for naval combat vessels[END_REF] only a few approaches feature the flexibility and capability needed to make such a system appealing to small and medium vessel producers. Some efforts are currently being made by open-source communities to bridge the gap between bringing such vital information from vessels to cloud infrastructure where it can be analyzed and fed back to the production aspect for better and optimal vessel design. One such opensource project is Signal K [START_REF] Signal | Signal K[END_REF] as well as the Universal Marine Gateway (UMG).

Subsequently, we describe an overview of Signal K as well as the Universal Marine Gateway (UMG) which provide different approaches to acquiring sensor data from vessels.

Signal K. Signal K is an open-source solution, driven by a community of sailing and marine enthusiasts. It strives to achieve an open data format for the maritime sector by using standard internet technologies on the vessel using a dedicated Signal K server. The complete solution is licensed under Apache v2.0 permissive open-source license. A significant advantage that the project accomplishes is the representation of the data from various heterogeneous sources of information. It is easily deployable on a standard laptop as well as different single-board computers (SBCs) and relies on standard internet technologies like REST, WebSocket, and TCP/IP suite for information exchange [START_REF]Signal K Documentation[END_REF]. The data format within Signal K is represented using JSON Schema with UTF-8 encoding, making it compatible with many standard IoT solutions.

UMG. The Universal Marine Gateway (UMG) is a data acquisition unit capable of interfacing various data busses as well as different sensors and has been developed through a series of collaborative research projects. At its hardware core is an industrialgrade single board computer with a customized operating system. The UMG hosts a time-series database, which stores the vessel data and allows for data curation. While the Signal K framework is more addressed towards the requirements of the consumer market, the UMG is an implementation for professional users. It comprises of a subsystem of UMG Nodes which handle data from various heterogenous data-sources (e.g., data buses and digital or analog sensors) and sends it to the UMG through an ethernet network deployed within the vessel. UMG Nodes provide deployment opportunities to place different sensors like accelerometers within the vessel at locations normally difficult to access like the hull or engine room.

Approach

The basis for a virtual sea trial is usually a computational fluid dynamics (CFD) simulation of the vessel model in relevant simulation space. Besides a well-defined vessel model, the data basis for this simulation is the crucial step towards running a credible virtual sea trial. This paper's approach is, therefore, to employ a flexible data acquisition system to capture a precise model of a sea trials parameters and deliver reliable data which can remove ambiguity from the current digital design process.

Virtualization Prerequisites

It is crucial to work from a concise set of requirements and expectations to create a meaningful model of a sea trial. Therefore, the first step in the virtualization process is to define these. It is essential to define the scope of the digitization. In general, the following three data scopes are differentiated:

1. Data describing the environmental input to the trial situation (e.g., wind speed, wave height) 2. Data describing the vessel's input to the trial situation (e.g., engine speed, heading) 3. Data describing the vessel's behavior in the trial situation (e.g., deformation, accelerations)

Once the scope has been decided for the sea trial at hand, the individual measurements and their parameters need to be defined. The process will involve selection of data sources (e.g., sensors, onboard systems) as well as their type of placement and calibration parameters.

Sea Trial Virtualization

With the data acquisition parameters finalized, the second step is the virtualization of a real-world sea trial. Besides the configuration of the data acquisition system to the requirements fixated in the first step, the mode of installation on the vessel must be prepared before the sea trial. Most important factors are the exact placement and fixation of the sensors as it is especially crucial for sensors which track physical variables, like vibration.

Before the actual sea trial occurs, the behavior of the vessel in the zero state condition shall be captured which implies to store data from all sensors with minimal environmental influence to capture the impact of the vessel itself on the readings and establish a baseline for measurements during the sea trial. It is recommended to do this for 15 minutes with the engine turned off and another 15 minutes with the engine at idle speed.

It is vital to ensure proper synchronization of the system time to allow the correlation of the data from the vessel with external data sources (e.g., weather service), and it is highly recommended to do this before the sea trial. During the sea trial, the live data can be monitored in real-time on the vessel or with minimal delay on an accompanying vessel or shore station.

After the end of the sea trial, it is recommended to repeat the zero state capturing procedure to document whether and if so, how the sea trial has impacted the installation.

Data Curation

After the sea trial, data curation is recommended and should consist of the following steps. First, the data validity needs to be checked. Besides quantitative consistency checks (e.g., on the required and real sampling frequency) also qualitative verifications (e.g., comparison of pre and post sea trial zero state conditions) shall be performed to ensure a high level of data quality. Subsequently, the data shall be annotated with events (e.g., time of a specific maneuver) and subjective findings (e.g., uncomfortable driving situation) from the sea trial. Finally, the data should be persisted both in a database for further use in analysis and simulation as well as in a report to summarise the sea trial.

Validation

For the validation of the sea trial virtualization procedure, an ongoing vessel development process of the Norwegian boat manufacturer Hydrolift1 has been selected. With the development of highly modular vessel platform, the sea trial virtualization experimented. Fig. 1 below gives an overview of the stages of the validation experiment.

Fig. 1. Sea Trial Virtualization Schema

In the beginning, a simulation space is created, which takes into consideration all relevant virtual parameters for a vessel. Such information is available from blueprints of the prototype vessels and engineering information knowledge banks. From the simulations, simulated parameters of the vessel are obtainable like the trim (angle between the vessel and the engine), pitch, and drag. The next step was to understand whether these parameters are measurable directly or indirectly from the vessel. Many parameters (like vessel orientation and engine values) are directly obtainable via the NMEA2000 interface and were collected via the UMG. The drag of the vessel in the water is usually measured indirectly through accelerometers measuring acceleration values at specific points of interest within the vessel. By mapping these parameters to the CFD simulations, the gap between simulation space and actual sea trial conditions is narrowed. The data collected on the UMG via interfaces like the onboard systems (NMEA2000) and different sensor deployment via the UMG Nodes was logged at a fixed time interval. Fig. 2 shows a snapshot of parts of the sea trial live dashboard.

Fig. 2. Sea Trial Dashboard

Post sea trial, the data was curated and used in comparative analyses. Comparison between the measured parametersnamely, pitch angles of the vessel versus speed, engine/fuel consumption versus speedwere found to provide a more sensible representation than the interpretation of individual measurements. Finally, based on the analyses, an optimization of the vessel can be planned and validated through further simulations while at the same time, the initial simulation space can be refined.

Conclusion

This paper has presented an approach towards the virtualization of sea trials for the vessel prototype testing process. The proposed procedure relies on the capturing and integration of vessel-related sensor data into a knowledge-based engineering process.

From the ideation and initial experimentation of the procedure first benefits for the vessel, developers have already been experienced in the form of increased understanding of the vessel's behavior under the test conditions. Also, the trust in the CFD simulation, which is being employed in the design process, has been increased. To fully assess the viability and universal applicability in the vessel manufacturing domain, further experimentation is needed and foreseen. At the same time, the range of measurements will be expanded to allow to extend the scope from mechanical product development and validation, e.g., towards the assessment of the real lifecycle costs and emissions.

https://hydrolift.com

Acknowledgments. The research leading to these results has received funding from the Research Council of Norway within the project "Robust Industriell Transformasjon -RIT" as well as the European Union's Horizon 2020 research and innovation programme under grant agreement No. 727982. The contents of this paper reflect only the authors' view, and the Commission is not responsible for any use that may be made of the information it contains.


Alireza Ashrafian
email: alireza.ashrafian@ntnu.no

Ole-Gunnar Pettersen

Kristian N Kuntze

Jacob Franke

Erlend Alfnes

Knut F Henriksen

Jakob Spone

Full-Scale Discrete Event Simulation of an Automated Modular Conveyor System for Warehouse Logistics

Keywords: Discrete Event Simulation, Discrete Event Logistics System, Warehouse Logistics, Distribution Center Automation, Material Handling, Modular Conveyor Systems, Decision Support System

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

Introduction

Automated modular conveyor systems are widely used in central warehouses and material distribution centers to achieve a high degree of throughput and smooth material flow while remaining flexible and efficient [START_REF]QuickMove: Flexible, modular conveyor system for small loads[END_REF]. Achieving high efficiency, however, is not always problem free. The higher capacity and increased complexity of interactions in smart material handling systems also require advanced analysis to avoid design mistakes and to ensure efficient operation. Factors such as varying demand and a high degree of randomness, this poses significant challenges in achieving desired ef-ficiencies and maintaining smooth material flows. Verifying that the system design adheres to operational requirements under constantly varying conditions is often beyond the capability of conventional analysis tools [START_REF] Jia | The Application of Simulation Technology in Distribution Center[END_REF].

In the past few decades, computer simulation has become one of the most effective decision support tools used in logistic systems. 3D transient computer simulations have provided reliable solutions to identify and avoid costly design and operational mistakes. Among them, discrete event simulation (DES) [START_REF] Law | Simulation Modeling and Analysis[END_REF] has become an indispensable tool for understanding the complex dynamics in logistics and supply chain systems [START_REF] Brailsford | Discrete-Event Simulation and System Dynamics for Management Decision Making[END_REF][START_REF] Tako | The application of discrete event simulation and system dynamics in the logistics and supply chain context[END_REF].

Moreover, and within the paradigm of Industry 4.0 digital transformation, computer simulations integrated with virtual reality systems and operation data, have become the basis of the digital twin technology [START_REF] Rodic | Industry 4.0 and the New Simulation Modelling Paradigm[END_REF][START_REF] Rosen | About The Importance of Autonomy and Digital Twins for the Future of Manufacturing[END_REF]. According to Stark et al. [START_REF] Stark | Innovations in digital modelling for next generation manufacturing system design[END_REF], "A Digital Twin is the digital representation of a unique asset (product, machine, service, product service system or other intangible asset), that alters its properties, condition and behavior by means of models, information and data". A Digital Twin is a digital abstraction of a system that is fed with live, high quality, data. The vision of a digital twin for smart warehouse systems is, therefore, to create a mirror of processes incorporating all related information within the warehouse system. The twin can be used to monitor processes in real time and continuously fine tune all operations in the warehouse in order to optimize the flow, to find bottlenecks, etc. In short, if such a digital twin is realized, it allows better understand end-to-end processes, find bottlenecks and improve performance beyond all expectations. The success of digital twins is, therefore, greatly dependent on, i.a., the performance of the underlying simulation model in capturing the key characteristics and complex dynamics of the system.

The purpose of this paper is to demonstrate how a full scale DES model can support the design and optimization of smart warehouse systems. The paper also clarifies what are the crucial aspects that must be taken into account in full detail in the model in order to create a reliable standalone decision support system. Moreover, the paper highlights the key steps that are yet to be taken to make the dynamical model a real Digital Twin that can be used as an effective tool for decision-making and optimization.

2

The automated modular conveyor system under study

The geometry and operation

The system under study is an automated modular conveyor system for handling totes, trays and cartons [START_REF]QuickMove: Flexible, modular conveyor system for small loads[END_REF] that has a complex interconnected geometry (Fig. 1). The system comprises of five major parts: the "supplier", the mini-load storage systems, the "highway", 5 sub-loops, and 21 pickup stations. The supplier feeds the system with totes that are stacked with products from a pallet. The highway is the central conveyor that runs through the system and makes it possible that totes fed by the supplier can travel between the sub-loops. Typically, a tote that comes from the supplier via the highway to a sub-loop will end up at a mini-load station for storage. Mini-loads are vertical storage units that store loaded totes and trays for later use. Each mini-load system is connected to a sub-loop in the conveyor system and has 5 input and output ports. Alternatively, if a tote enters the system through a mini-load, it will usually go to one of the four pickup stations connected to the associated sub-loop. These pick up stations hold a tote for a statistically certain amount of time before it is released to its next destination, e.g., another pick up station.

All routing operations are fully automated by scanner and barcodes. In front of each decision point, the control system sends a totes barcode to a bin destination manager (BDM), which immediately returns a command telling the control system if the tote should proceed straight ahead or change direction. The BDM is a part of the central warehouse management system (WMS). Each module has a constraint on capacity, i.e., it can contain a limited number of totes in order to function properly. Congestion occurs if this limit is exceeded and, consequently, the smooth function of the system will be impaired. To prohibit congestion, all of the entrances to and the exits from the areas are equipped with optical counters that control the number of totes in that section. The system automatically blocks further incoming of totes if the maximum capacity is reached.

Operational challenges

The system is designed such that the bottleneck in the system always resides at the pickup stations. This is to ensure the continuous flow of totes in other parts of the system and, to ensure that totes are always available for the operators at pick up stations. The operators have a relatively high operational cost, and they should never be idle.

At times, during periods of increased strain on the system, the highway often becomes the bottleneck and the flow of totes is disrupted. Several observations from operating the system suggested that the bottleneck appeared on the highway due to its capacity limitation as well as the way the supplier feeds the highway which takes a lot of its capacity. The current simulation modeling and analysis was therefore aimed at finding a new way of feeding the totes from the supplier to the system and, support the corresponding design changes with data-driven analysis.

3

The full-scale dynamical model

The simulation modeling and analysis software FlexSim® [START_REF] Beaverstock | Applied Simulation: Modeling and Analysis Using FlexSim[END_REF] has been used in order to perform a full scale DES model of the automated modular conveyor system under study. The model geometry is based on the 3D CAD data and is identical to the real system in operation (Fig. 2). Fig. 2. The full-scale computer simulation model for the installed modular conveyor system.

The model logic

The operational logic of the system has several important aspects that are carefully implemented in the model. There are certain variables tagged to each individual tote that contains information about its current state and destination, as well as an individual name that is used for statistics collection and, later on, for model verification. These variables come into play at every crossroad where decision points are located. Right before every crossroad, where a tote is faced with several different paths to continue on, there is a decision point that contains a code attributed to the variables in each tote to decide whether to continue on to one conveyor or another. A decision point may also change the variables containing information about the totes destination, e.g., by choosing what pickup station to travel to right after entering a sub-loop.

As mentioned before, when a tote has arrived at a pickup station, it will be held at that station for a certain amount of time. This stay time varies statistically depending on the work of operator at that station. The pickup station also changes the information of the tote upon its release. For example, when the pickup station labels a tote as "empty", then the decision points are forced to direct the tote to the fastest available route to exit the system. The final part of the logic of the operation deals with the function of suppliers which provide the model with totes based on statistical distributions that change over time (see §3.2). They also create and set the value of the variables in the tote. The value of these variables is also determined by time-dependent statistical distributions. The associated operational logic explained above is programmed in each corresponding individual element of the model using the script language provided by the FlexSim® software [START_REF] Beaverstock | Applied Simulation: Modeling and Analysis Using FlexSim[END_REF].

Simulation input and statistical modelling

Various time-dependent statistical data from the operation is integrated with the model. The data was collected from the system over one week of operation amounting to approximately 500,000 totes. The data included the number of totes arriving at the system per hour, and the number of totes being processed by the system per hour.

Moreover, statistical analysis was performed in order to model the statistical distributions [START_REF] Law | Simulation Modeling and Analysis[END_REF] for the various number of input variables such as manual feeding rate to the supplier, feeding rates from the mini-loads, processing time at pick up stations, and the decision points that distribute the totes throughout the system. Hourly based operational data indicated a high degree of dynamics in the variations associated with these statistical distributions. A considerable effort was made in order to correctly model these variations and program it in the DES model.

Model verification and validation

Several verifications of the model were performed under controlled conditions in order to ensure the consistency of the model and its internal logic. Validation of the model including various statistical models was also performed by comparing the simulation results with operational data for several variables. To ensure the consistency of the model in terms continuity between all geometrical areas, the totes resident time in the system were calculated and it was verified that no tote remains in the system for a large amount of time. The statistical correctness of decision points in sending totes to various destinations was validated by comparing the simulation results for the cumulative number of totes ended up at various sub-loops with the operational data after several hours of operation (Fig. 3).

Results and the improved design

Various simulations were performed considering multiple scenarios for the design modifications in the system. The resulting scenario that had the most effect on removing the bottleneck from the highway and therefore improving the smooth material flow in the system is presented here. The reference case is the one identical to the conditions in the original system. The analysis was based on various key performances in the system, e.g., average content in the highway and the sub-loops, idle time for the operators at pick up stations, and passages through certain decision points along the highway. These give clear indications of how the implementation of the suggested design modification would affect the system.

The proposed new design (Fig. 4) contains a new loop where the newly stacked totes would go alongside the highway and further on to the allotted sub-loops, and finally, the mini-load for storage. The basic idea was to ensure the increased availability of totes for the pickup station, while reducing the average number of totes on the highway, and hence, avoiding congestion. It should be noted that the time-dependent statistical data for the number of totes arriving at the system as well as the statistical distributions for e.g., manual feeding to the system remains unchanged and equally applicable to the system with new design as well. The average content in the entire system dropped by about 8% percent. Results for the highway and each of the sub-loops are shown in Fig. 5. Note that the number of totes processed in each sub-loop has remained almost unaffected. The number of totes passing through the decision points on the highway which dropped by a staggering 28% (not shown here) which clearly indicates a significant reduction in the traffic on the highway. Another advantage of the design is that if one of the sub-loops are full, the tote could loop around instead of congesting the highway. Moreover, an increase in the availability of the totes to the operators without consuming further from the available area in the facility was obtained. Fig. 6 shows a heat map of the average tote content on the highway. The reduction in the number of totes on the highway is evident, especially, in the area closest to the entry from the supplier (the right end of the figure). The analyses point out the apparent reduction of strain put on the system and a more uniform distribution of totes along the whole highway conveyor.

Conclusions

Highly automated logistics systems incorporate enhanced capacity and efficiency as well as a higher degree of complexity in system dynamics. To this end, advanced computer simulation models are of great value to create near-reality standalone models for complex discrete event logistics system (DELS). The model presented in this study has captured the complex geometry, the variety of system interactions, the system logic, and the key stochastic dynamics that took place under the real operation. It was demonstrated that the current model could be used as a reliable standalone decision support system for operation and design change. Throughout the course of this study, some requirements were also identified in order to make conventional simulation modeling an effective tool for Digital Twins. Firstly, connectivity and integration with operational or enterprise resource planning database must be realized. Secondly, and as demonstrated in this study, the modelled system for Digital Twin application must include high level of details and low level of abstraction in all the aspects including geometry, stochastic and logic. Ultimately, the construction of models, the process of optimization through design modifications, development of model versions, and the construction of various scenarios should be automated to the highest degree in order to achieve efficiency without manual interventions. Moreover, the need for functionality was of significant importance in the course of conducting the current study. Manual modifications of the simulation model can be time consuming, especially if a large set of variations of the model needs to be built.

Fig. 1 .

 1 Fig. 1. The geometry of the modular conveyor system under study (Courtesy of ASKO and Swisslog).

Fig. 3 .

 3 Fig. 3. Cumulative number of totes sent to various sub-loops (SLs) by the decision points.

Fig. 4 .

 4 Fig. 4. The proposed new design including a new conveyor loop marked in red.

Fig. 5 .

 5 Fig. 5. Average number of totes per hour on the highway and sub-loops.

Fig. 6 .

 6 Fig. 6. Heat maps indicating the average number of totes on the highway conveyor. Top: base case, bottom: the new design.


Shota Suginouchi
email: suginouchi@ise.aoyama.ac.jp

Hajime Mizuyama

Scheduling Auction: A New Manufacturing Business Model for Balancing Customization and Quick Delivery

Keywords: Production Scheduling, Auction, Mechanism Design

This paper proposes a new manufacturing business model that is enabled by a novel scheduling method based on an auction, called Scheduling Auction. The proposed scheduling method and the associated business model aim to balance expanding customization and quick delivery to better satisfy customers. It accepts bids from customers, captures their preferences in terms of product type and due date more extensively than before from the bids and reflects those preferences in the production schedule. The fundamental framework of the method and its working are illustrated with a simple case of a factory with a single machine. Future research directions include conducting validation experiments with human subjects and extending the method to more complex production systems.

Introduction

Manufacturing companies must meet customers' demands. To satisfy diverse market demands, production styles have been continuously changed from highvolume low-variety production to low-volume high-variety production. This trend keeps increasing product variety, and yet the requested due dates never become longer. Thus, companies face a crucial challenge of how to balance expanding customization and quick delivery to satisfy their customers.

The conventional manufacturing business model can be broadly classified into make-to-stock (MTS) and make-to-order (MTO) models. MTS is characterized by a short lead-time from order to delivery, where quick delivery is accomplished in exchange for holding product inventory. Because the cost and risk associated with holding inventory increase with increasing product variety, naive MTS is not suitable for most extremely high-variety production environments currently in use. MTO, however, deals with high-variety production with minimal inventory, by making the production schedule after receiving orders. However, the resultant longer lead-time to delivery is not always accepted by the market. Mass customization is a relatively new business model that combines MTO of final product assembly with MTS of standardized parts and units, thereby making the lead-time shorter than that of naive MTO with less inventory than naive MTS. However, demanding customers remain unsatisfied.

Thus, this paper proposes a new manufacturing business model that is enabled by a novel scheduling method based on an auction, called Scheduling Auction. The importance of scheduling is well recognized even in conventional MTO and mass-customization, where the problem is captured as an efficient allocation of jobs to machines along the time axis. The jobs considered here correspond to a set of orders provided by customers, which specify product type and due date. Thus, when zooming out back to each customer's perspective, another relevant decision is made in advance by the customer in terms of which product type and due date should be claimed. The proposed method is unique in that it deals with these two decision problems simultaneously by involving customers in the computational process, and it tries to obtain a production schedule preferable to the customers.

In Scheduling Auction, customers are invited to join an auction and bid on a product type and due date. A schedule is then made according to the bids collected from the customers. In conventional production scheduling, an order from a customer is specified by a single set of values for the product type, price, and due date, e.g., "Product A, $100, and within two weeks". In the proposed method, in contrast, the preference of the customer can be expressed more flexibly, e.g., "I want to buy Product A or Product B but not both. Product A is better than Product B, if it is delivered until the end of this month." The flexibility expands the solution space of the corresponding scheduling problem, and is expected to lead to a superior solution. This paper presents the fundamental framework of Scheduling Auction by using a simple case of a factory with a single machine.

Literature Review

Some papers have proposed a production scheduling method based on an auction mechanism. For example, Kutanoglu and Wu [START_REF] Kutanoglu | On combinatorial auction and Lagrangean relaxation for Distributed Resource scheduling[END_REF] used a combinatorial auction and Lagrangean relaxation for scheduling. In this study, auctioned goods are the right to occupy a machine in a specified time slot. Each job is treated as an agent and bids for a combination of time slots for processing itself. The auctioneer coordinates the bids by adjusting the price of each time slot according to the conflicts among the bidders. The primary motivation behind these studies is how to solve a scheduling problem through distributed computation rather than how to capture and incorporate customers' detailed preference into the schedule.

Suginouchi et al. [START_REF] Suginouchi | Value co-creative manufacturing system for mass customization: Concept of smart factory and operations method using autonomous negotiation mechanism[END_REF][START_REF] Suginouchi | Utilization of pheromone in production scheduling by negotiation and cooperation among customers[END_REF] utilized a combinatorial auction for taking customers' preferences into consideration in scheduling. This study addresses the scheduling problem by repeating auctioning among computer agents representing customers, which imitates a negotiation process among customers. A sub-optimal solution for the corresponding combinatorial optimization problem is obtained by a math-heuristic approach [START_REF] Boschetti | Matheuristics: Optimization, simulation and control[END_REF]. To more accurately capture customers' preference, it is desirable to have the actual customers participate in the negotiation process. However, it is not realistic for actual customers to follow the troublesome process of repeating an auction many times. Furthermore, strategic interaction among the customers needs to be properly managed to involve them into the computational process. Mechanism design is an academic field addressing strategic interactions among self-interested agents, and it has recently started to be applied to production scheduling [5][START_REF] Christodoulou | Mechanism design for scheduling[END_REF][START_REF] Kress | Mechanism design for machine scheduling problems: Classification and literature overview[END_REF]. Most studies in this field focus on the theoretical aspect and deal with simplified settings, where either machines or jobs are simply deemed as self-interested agents, and the earlier decision of having customers choose product type and due date is not incorporated. There have also been some initial application studies. For example, Zhong et al. [START_REF] Zhong | ADMM-based distributed auction mechanism for energy hub scheduling in smart buildings[END_REF] and Zheng et al. [START_REF] Zheng | Mechanisms for optimally scheduling and pricing pleasingly parallel jobs in service clouds[END_REF] applied the auction-based scheduling method to energy hub and service clouds, respectively. Nishino et al. [START_REF] Nishino | An auction mechanism considering seat reservations in movie theater services[END_REF] proposed an auction mechanism for arranging seat reservations in a movie theater. However, all the settings considered in these studies are different from the one for Scheduling Auction.

• Bid Value • Product • Due date • Bid Value • Product • Due date • Bid Value • Product • Due date

Proposed Method

It is assumed that a manufacturing company divides a planning horizon into multiple terms, and holds an auction to plan the production schedule of every term. This paper focuses on an auction held for a certain term. The term is further divided into smaller time units, and due dates, processing times, completion times, etc., are all specified in terms of the units in the following.

Fig. 1 shows a schema of the proposed method, Scheduling Auction. The manufacturing company takes the role of the auctioneer, and customers are treated as bidders. Auctioned goods are not products but combinations of product type and due date. The customers' preference of the goods is assumed to be quasilinear.

There are some bidders, and each bidder may submit multiple bids for different combinations of product type and due date. When two or more bids are submitted by a bidder, only one of them can be assigned to the schedule. Because the production capacity is limited, not all customers can receive a product. If all bids made by a customer are not chosen by the auction, the customer may join the next-term auction or give up obtaining a product.

When bids are collected, then the auctioneer determines the winning bids among them and establishes the corresponding production schedule. The criterion of this winner determination is to maximize the social surplus. The contract price is determined by using the Vickrey-Clarke-Groves (VCG) mechanism [START_REF] Vickrey | Counterspeculation, auctions, and competitive sealed tenders[END_REF][START_REF] Clarke | Multipart pricing of public goods[END_REF][START_REF] Groves | Incentives in Teams[END_REF], to induce truthful behavior of the bidders.

Example Case

In this section, a single-machine scheduling problem is used to illustrate the working of the proposed method.

Notation

i : Customer (Bidder) (i = 1, . . . , I) t : Product type (t = 1, . . . , T ) P T t : Processing time of type t d : Index of possible due dates (d = 1, . . . , D) DD d : Due date corresponding to index d b i,t,d : Customer i's bid value on type t and due date index d p i : Customer i's payment x i,t,d ∈ {0, 1} : A decision variable indicating whether b i,t,d is accepted j : Job processed in the jth (j = 1, . . . , I) jn j : The customer ordering the product corresponding to job j (Decision variable) CO j : Completion time of job j P O j : Processing time of job j DO j : Due date of job j SS : Social surplus SS -i : Social surplus in the absence of customer i

Formulation

The following formulation specifies the problem of optimizing the production schedule and determining winners of the auction at the same time. max

I i=1 T t=1 D d=1 b i,t,d × x i,t,d (1)
CO j = P O j (if j = 1) CO j-1 + P O j (otherwise) (∀j) (2)
DO j = T t=1 D d=1 DD d × x jnj ,t,d (∀j) (3) s.t. CO j ≤ DO j (∀j) (4)
jn j = jn j (∀j, ∀j |j = j ) (5)

P O j = T t=1 D d=1 P T t × x jnj ,t,d (∀j) (6) T t=1 D d=1 x i,t,d = 1 (∀i) ( 7
)
jn j ∈ {1, • • • , J} (∀j) (8)
x i,t,d ∈ {0, 1} (9)
The objective function is expressed by Equation ( 1), which maximizes the sum of the accepted bid values. Equation (2) defines the completion time CO j of job j. Equation (3) defines the due date DO j of job j. Equation (4) guarantees that job j completes before its due date DO j . Equation (5) guarantees that job j is processed only once. Equation [START_REF] Christodoulou | Mechanism design for scheduling[END_REF] expresses that the processing time of job j is determined by the product type t chosen by customer jn j . Equation [START_REF] Kress | Mechanism design for machine scheduling problems: Classification and literature overview[END_REF] guarantees that the product type t and due date DD d of customer i are determined uniquely.

Algorithm

In the case of the single-machine scheduling problem, the algorithm of Scheduling Auction is as follows:

STEP 1 Customers who want to order a product participate in the auction as bidders. Each bidder may bid for all combinations of product type t and due date DD d . Table 1 shows an example of such bids, where the number of product types T is three and the number of possible due dates D is two. STEP 2 The auctioneer gathers bids and solves the integer optimization problem formulated in section 3.2 to determine the winning bids and to plan a production schedule. The social surplus SS is also obtained. STEP 3 The auctioneer determines each bidder's payment p i using the VCG mechanism [START_REF] Vickrey | Counterspeculation, auctions, and competitive sealed tenders[END_REF][START_REF] Clarke | Multipart pricing of public goods[END_REF][START_REF] Groves | Incentives in Teams[END_REF]. To do so, i is initialized to be 1.

STEP 4

The auctioneer calculates the social surplus SS -i in the absence of customer i by solving the integer optimization problem formulated in section 3.2. Equation ( 10) determines customer i's payment p i .

p i = SS -i -(SS - T t=1 D d=1 b i,t,d × x i,t,d ) (10)
STEP 5 If i = I, i := i + 1 and go to STEP 4. Otherwise, the auctioneer ends the auction.

Computational Experiments

To discuss the performance of Scheduling Auction, numerical experiments were conducted. The experiments were performed with the conditions shown in Table 2. This study used a CPLEX 12.6.3 [START_REF]IBM Web page[END_REF] and Inter(R) Core i7 3.50GHZ 16.0GB memory computer. Fig. 2 shows a schedule obtained by Scheduling Auction when the number of customers I is 5, the number of product types T is 2, and the number of possible due dates D is 2. The objective function value corresponding to the social surplus is $336. Table 3 shows the bid values. * signifies accepted bids. In this case, customer 1 pays $5, customer 2 pays $29, customer 3 pays $29, customer 4 pays $0, and customer 5 pays $19.

The mean calculation time for solving a problem was 0.03 s in 10 trials. The calculation time increased exponentially as the number of customers I, the number of product types T , and the number of possible due dates D increased. For example, the mean calculation time was 22.02 s in 10 trials when I = 10, T = 2 and D = 2. When I = 10, T = 2, and D = 10, the mean calculation time for solving optimizing a problem was 96.06 s in 10 trials. When I = 10, T = 10, and D = 2, the mean calculation time was 180.58 s in 10 trials. The exact solution cannot be obtained in cases where I is more than 15. Table 3. Each Bidder's Bid Values Furthermore, to decide each customer's payment, the auctioneer must solve the integer optimization problem shown in section 3.2 (I + 1) times. Thus, an efficient optimization method is required to realize Scheduling Auction.

t = 1 t = 1 t = 2 t = 2 Customer i d = 1 d = 2 d = 1 d = 2 1 *

Conclusion

This paper proposes an auction-based scheduling method and a new manufacturing business model enabled by the method. The proposed method captures customers' preferences in terms of product type and due date more extensively than before from their bids, which is then reflected in to the production schedule. A winner determination procedure is also presented.

This paper uses a simple case of only one machine to describe the fundamental framework of the proposed scheduling method and the associated business model. Future research directions include conducting experiments with computer agents and/or human subjects and extending the problem formulation and solution procedure to more complex production systems.

Furthermore, this paper implicitly assumes that customers' preferences are determined only by the characteristics of the final product, such as specification, price, and due date. In the future, however, customers may also care about how the product is produced, i.e., the production process itself. A typical example is observed in the liberalized energy market. In April 2006, the retail electricity market in Japan was liberalized, and consumers were enabled to select a retailer. Consumers' selection criteria were found out to include not only the price but also the greenness, etc. Some retailers started to appeal to consumers by the method of generating electricity, e.g. their energy was not generated from nuclear power plants but from renewable energy sources, etc. Thus, future customers may have a preference in terms of the energy utilized to produce a product, and such a preference could also be incorporated into the production schedule by extending the method proposed in this paper.

Fig. 1 .

 1 Fig. 1. Schema of Scheduling Auction

Fig. 2 .

 2 Fig. 2. Example of an Obtained Schedule (I = 5, T = 2, D = 2)

Table 1 .

 1 An Example of Bids

		Product type t
	Due date DD d 1	2	3
	DD1	$200 $100 $50
	DD2	$150 $100 $0

Table 2 .

 2 Experimental Conditions

	Number of customers I 5, 10 Bid value bi,t,1	$U [1, 100]*
	Number of types T	2, 10 Bid value b i,t,d (d ≥ 2) max{U [1, 100], b i,t,d-1 }
	Number of possible
	due dates D	2, 10 Processing time P Tt t
	Number of trials	10 Due date DD d	ceil{(d + 1) × T ×I D } **
	* U [a, b]: random sampling from a uniform distribution
	** ceil(x): roundup integer

Acknowledgements This research was partially supported by the Ministry of Education, Science, Sports and Culture, Grant-in-Aid for Young Scientists, 2019-2022 (19K15240, Shota Suginouchi).


Giuseppe Ismael Fragapane
email: giuseppe.fragapane@ntnu.no

Aili Biriita

Jan Ola Strandhagen
email: ola.strandhagen@ntnu.no

Possibilities and benefits of using material flow information to improve the internal hospital supply chain

Keywords: Material flow, Automated Guided Vehicle, Hospital

The concept of Supply Chain Management has become increasingly important in healthcare and notably in hospitals. Information along the supply chain is the key element for analysis and improvement purposes. The aim of the study is to analyze and visualize the material flow at a Norwegian hospital to identify the possibilities and benefits in current and future planning and operation. The integration of IT enables combining material and information flow. Statically analyses of the material flow can support in planning and control of the logistics activities. The visualization of the material flow can support to take long-term decisions e.g. for distributing departments at the hospital.

Introduction

The concept of Supply Chain Management (SCM) has become increasingly important in healthcare and notably in hospitals. The primary objective of SCM is "to integrate and manage the sourcing, flow, and control of materials using a total systems perspective across multiple functions and multiple tiers of suppliers" [START_REF] Monczka | Purchasing and supply chain management[END_REF]. While literature concerning SCM on a strategic level is extensive, less academic literature focuses on tactical and operational challenges particular to the healthcare industry [START_REF] Mckone-Sweet | The ailing healthcare supply chain: a prescription for change[END_REF]. One of the key challenges and barriers for effective SCM in the healthcare industry is the lack of capital to build a sophisticated Information Technology (IT) infrastructure supporting supply chain operations [START_REF] Burns | The health care value chain: producers, purchasers, and providers[END_REF]. Information along the supply chain is the key element for analysis and improvement purposes. In order to benchmark and measure performance improvements, it is important to define measures that track both material activities and related costs, and supply chain performance related to the core activity, patient care [START_REF] Mckone-Sweet | The ailing healthcare supply chain: a prescription for change[END_REF].

A recent literature review by Volland et al. [START_REF] Volland | Material logistics in hospitals: a literature review[END_REF] suggests that the availability of information across the supply chain can lead to more integrated supply chain concepts for hospitals. There is a need to understand how consistent IT systems and data standards across hospitals can improve the hospital supply chain. Several studies have shown that IT systems can improve purchasing decisions and reduce costs lowering inventory levels [START_REF] Kumar | Rx for smart hospital purchasing decisions: The impact of package design within US hospital supply chain[END_REF]. However, using SCM approaches in hospitals have proven to be more complex. The hospital supply chain has to manage a variety of product and service enterprises including medical consumables, pharmaceuticals, catering, laundry cleaning, waste management, home-care products, information technology, vehicle fleet management and general supplies [START_REF] Gattorna | Strategic supply chain alignment: Best practice in supply chain management[END_REF]. Norwegian hospitals have started centralizing and automate both the external and especially the internal material flow [START_REF] Ullrich | Automated guided vehicle systems[END_REF][START_REF] Fragapane | Material Distribution and Transportation in a Norwegian Hospital: A Case Study[END_REF]. Consequently, the IT and automated transportation systems collect and align a large amount of information across the supply chain.

A previous study on internal logistics in a Norwegian hospital visualized the material and information flow in order to analyze the current situation of the supply chain [START_REF] Fragapane | Material Distribution and Transportation in a Norwegian Hospital: A Case Study[END_REF]. Several IT and sharing systems are used and materials can be tracked and controlled throughout the internal supply chain. However, the IT and automated transportation systems create a huge amount of data, mainly for monitoring and controlling purposes. This data is often not further used or analyzed for improvement purposes and is typically known as 'idle data' [START_REF] Schmidt | Industry 4.0 -Potentials for Creating Smart Products: Empirical Research Results[END_REF]. Increased computing power has facilitated the possibilities of using data analytics to discover patterns and improvement possibilities from datasets where a human would not necessarily have found a pattern.

This study aims at answering the following questions: How can visualization of material flow information improve the internal supply chain of a hospital? Further, how can the hospital supply chain benefit from an integrated IT system? The study will be based on the data collection from the previous study at a Norwegian hospital further explained in section 3.

The paper is structured as follows: Section 2 provides a background on related literature within hospital logistics and SCM. Section 3 presents an overview of the case hospital supply chain, followed by a description of the methodology. In section 4, the results of the statistical analysis are presented, which are further discussed in section 5. This research will end with recommendations for further improvements for the case hospital and future research within this topic.

Theoretical background

Over the past 20 years, there has been an increase in healthcare expenditures in all OECD countries [10], which has motivated to improve the healthcare sector. A study by Poulin et al. [START_REF] Poulin | Benchmarking the hospital logistics process[END_REF] state that half of the logistics costs can be eliminated with efficient logistics management. Furthermore, Volland et al. [START_REF] Volland | Material logistics in hospitals: A literature review[END_REF] state that increasing logistics efficiency might not directly influence patient care but may give medical staff more time for patient related activities. It is important to recognize that logistics activities have an impact on the overall performance of the hospital [13].

Polater et al. [START_REF] Polater | An investigation of government and private hospitals' supply chain management[END_REF] describe a hospital supply chain as "a complex system that requires the flow of products and services, in order to satisfy the needs of those who serve patients", with the aim to "deliver products at the right time, for the purpose of fulfilling the requirements of those providing healthcare". The hospital can be divided into an external and internal supply chain. The internal supply chain has to establish its own logistics network that correlates with external suppliers in order to supply the customer, in this case, the patient [START_REF] Rivard-Royer | Hybrid stockless: a case study: Lessons for health-care supply chain integration[END_REF]. According to Volland et al. [START_REF] Volland | Material logistics in hospitals: A literature review[END_REF], the external supply chain has been received the most attention, while the internal supply chain is the weak spot in the hospital supply chain. Granlund & Wiktorsson [13] argue that the performance of the internal supply chain highly affects the overall performance, and remark the importance of continuous improvement in order to achieve increased competitiveness.

The focus of internal logistics is to provide supplies to the company's core activities, which for hospitals is patient care. Logistics and material handling are described as an extensive and important part of the healthcare sector, but it is far from the sector's core competencies [13]. A high variety of materials is often required in a hospital, which usually leads to complex logistics activities. In addition, different departments are responsible for various procurement activities, e.g. food services are responsible for the food supply, while pharmacy services are responsible for managing the procurement of pharmaceuticals, etc. [START_REF] Poulin | Benchmarking the hospital logistics process[END_REF][START_REF] Ozkil | Service robots for hospitals: A case study of transportation tasks in a hospital[END_REF], further increasing the complexity of SCM.

Introducing automation to logistics activities in the healthcare sector is viewed as a measure for improving efficiency and productivity [START_REF] Bačík | Pathfinder-Development of Automated Guided Vehicle for Hospital Logistics[END_REF]. The main challenges are knowledge transfer and adoption to technology, rather than the absence of available technology. Due to the complexity of the system, it is often difficult to establish central management and a consensus regarding the purpose of the system fitting all actors. It is crucial to distribute tasks and responsibilities appropriately, demonstrate the ability to have a comprehensive view on the planning process and to implement strategies and technologies on the basis of long-term benefits [13].

Several decisions and changes in hospitals have made the Automated Guided Vehicle (AGV) system a success and standard for material transportation in Norwegian hospitals. An AGV system can be defined as a driverless transportation system that is used for horizontal movement of materials, allowing for flexible material handling [START_REF] Vis | Survey of research in the design and control of automated guided vehicle systems[END_REF][START_REF] Mehrabian | Multi-objective routing and scheduling in flexible manufacturing systems under uncertainty[END_REF]. Some of the advantages of implementing an AGV system are increased productivity and flexibility, cost efficiency, savings in labor costs, reduced emissions and energy consumption, and improved safety [START_REF] Bechtsis | Sustainable supply chain management in the digitalisation era: The impact of Automated Guided Vehicles[END_REF]. In general, success factors for the implementation of automation in hospitals include benchmarking, learning from other industries, involving hospital personnel, and identifying the internal logistics functions. Therefore, available data about hospital logistics is crucial. While there has been a strong interest in establishing patient data, data of material flow are still less used and analyzed.

The Nordic countries are leading in eHealth applications. The number of general practitioners using electronic health records is among the highest in Europe [START_REF] Bergstrøm | Information technology strategies for health and social care in Norway[END_REF]. While it is well known that IT in healthcare and social services have the potential to improve the welfare and efficiency of systems, the potentials of IT technologies to improve hospital logistics are still scarcely explored in hospitals. According to Johns [START_REF] Johns | Integrating information systems and health care[END_REF], Information and communication technology (ICT) is an important tool for increasing competitiveness in the healthcare industry. Furthermore, ICT can help for total integration of healthcare systems.

Methodology

This study has used the mixed methods approach, combining qualitative and quantitative methods in order to strengthen the credibility of the results. More precisely, the methodology used is triangulation being one of the most common mixed methods designs. It is described as "the use and combination of different methods to study the same phenomenon" [START_REF] Chris | Case research in operations management[END_REF] and aims to "validate quantitative statistical findings with qualitative data results" [START_REF] Hesse-Biber | Mixed Method Research -Merging Theory with Practice[END_REF]. The research design consists of a case study and literature study.

A literature study was conducted to establish the state-of-the-art on hospital supply chain, internal transportation in hospitals, and IT supporting hospitals. Observations and semi-structured interviews were performed to give an understanding of the case hospital and internal supply chain. The case study was carried out in a large Norwegian hospital that has a capacity of 800 beds. The hospital implemented and launched an automated material handling system in 2009. Today, the automated material handling system consists of 21 AGVs, transporting approximately 50-70 tons of goods every week between 114 pick-up and delivery stations in different buildings, at different levels and departments. Daily, 500-650 containers are transported by the AGV system. The AGV system is operated with a centralized structure.

A transportation schedule for the AGV system has been defined based on simulations, hospital layout, and battery management and maintenance. This schedule has been adapted to changing demand of goods over the years. Orders are dispatched to the nearest AGV. This dispatching rule was chosen to reduce AGV idle transportation time.

The hospital has integrated a radio frequency communication system, connecting the different buildings, doors and elevators with the AGV system. The AGV can lift and move the wagons within the 4500-meter guide-path that connects all departments. AGVs can operate continuously for approximately three hours and is then sent to be charged for one hour.

Several individual and group interviews have been conducted with employees from both the operating and planning departments of the hospital. The positions of the interviewed hospital employees are Technical System Administrator, Supply Planner, Operating Manager, and Operating Technician. Based on the data received from the case hospital, a statistical analysis of the material transportation was conducted. The data represents the internal material transportation from October 2017 until March 2018.

Analysis

Based on the data retrieved from the AGV system of the case hospital, information about the material transportation were analysed to investigate the variety of transportation times and locations within the hospital. Fig 1 shows the average waiting and delivery times during the main hospital operation hours. During these hours, the following material groups are transported and supplied within the hospital: consumer and medical goods from both external suppliers and the central warehouse, waste, laundry, sterile goods, and food.

Discussion and conclusion

The hospital supply chain has high requirements for quality of services where timely delivery of materials is one of them. Lack of timely delivery of materials can have negative effects for a patient seeking treatment and care in a hospital. Sterile goods used in the operation departments must be delivered on time to ensure availability for any planned or acute medical operation. Patients need to receive their food within a certain time period in order to be ready for further operations or treatments.

The statistical analysis shows the transportation time of materials by AGVs in the case hospital. In general, the total transportation time varies throughout the day. While the average delivery time is rather stable, the waiting times are quite long at certain periods of the day. This can decrease the delivery precision of materials, which can lead to negative consequences for the patients. It can be argued that the waiting time is influenced by both transportation volume and AGV capacity in the specific periods. Balancing the transportation volume or combining deliveries to reduce the transportation volume are possible approaches for achieving timely delivery. This is possible since materials and respectively material groups transported and supplied during the operation hours of the case hospital varies strongly.

Changing the timely supply of materials can have critical consequences in hospitals. According to Abdulsalam et al. [START_REF] Abdulsalam | Health Care Matters: Supply Chains In and Of the Health Sector[END_REF], the healthcare supply chain is more fragile on critical supplies compared to other industries. Some materials and products must always be available in case of an emergency. It can be critical or fatal if the patient cannot receive instantly treatment due to missing supplies. Even though customization is an increasing trend in most industries, some supplies in the hospital supply chain are highly customized and are one-of-kind products for one certain patient [START_REF] Dobrzykowski | A structured analysis of operations and supply chain management research in healthcare (1982-2011)[END_REF][START_REF] Rimpiläinen | Modeling and Simulation of Hospital Material Flows[END_REF]. Often are a large number of actors involved in the hospital supply chain with different interests when supplying materials or products [START_REF] Abdulsalam | Health Care Matters: Supply Chains In and Of the Health Sector[END_REF]. Thus, the hospital supply chain can be described as highly fragmented, which can prevent from operating as a system [START_REF] Rimpiläinen | Modeling and Simulation of Hospital Material Flows[END_REF].

In general, not all materials moved within hospitals can be captured by the ICT system. Centralizing the material flow with the AGV system in a hospital can support to link the information along the supply chain and visualize the main material movements. The statistical analysis and the visualization of the material flow within the case hospital provide new opportunities to improve the timely delivery of materials and the performance of the internal hospital supply chain. Information derived from the statistical analysis supports the identification of material groups that can rather be moved outside of the overloaded operation hours. The traceability and tracking of materials within the case hospital have been demonstrated in a previous study [START_REF] Fragapane | Material Distribution and Transportation in a Norwegian Hospital: A Case Study[END_REF]. The integration of IT technologies in hospitals enables to combine the material and information flow along the supply chain and therefore to identify critical products. These measures help to identify and decide whether products can be transported in a different period or not. Visualizing the material flow in hospitals can support decision-makers in scheduling the material flow on a tactical level to balance the internal material flow to increase the timely delivery of materials. In the case hospital, the material traffic is strongly one-sided distributed.

The current policy in several countries is to expand larger hospitals and restructure them, and simultaneously close smaller hospitals [START_REF] Giancotti | Efficiency and optimal size of hospitals: Results of a systematic search[END_REF]. The insights of the material flow information can support in deciding where to place new departments or where to move current departments. Decisions on centralizing or decentralizing, insourcing or outsourcing departments or inventories, and changing the time period of internal material supply can also be based on a statistical analysis of material flow. Future research should focus on how to link information both material flow and electronic health records to further improve decision-making and performance of hospitals. Thus, electronic health records can be integrated and be input for forecasting and planning the material flow.

6

Fig. 1 .

 1 Fig. 1. AGV transportation time during an average day of operation.

Fig. 2 .

 2 Fig. 2. Material groups transported and supplied within the case hospital during an average day The pattern of transported goods is documented and clearly recognizable (see. Fig 2). Further, the material data provide insights about material supply during the different weekdays. The daily average amount of containers picked and delivered by the AGV at the different buildings within the hospital can be seen in Fig 4. Goods arrival and disposal is located in building 10, and clearly represents the highest volume of hospital material movement. Building 2 follows where the kitchen, central sterilization service, and the emergency department are located. Laboratories and specialized departments of the hospital are located in the rest of the buildings, with a considerably lower volume of material movement.

Fig. 3 .Fig. 4 .

 34 Fig. 3. Average amount of containers picked and delivered at the different buildings within the case hospital per day. Fig. 4. Amount of containers picked and delivered, visualized in the case hospital layout.

Acknowledgement

This research received funding from the strategic research area NTNU Health in 2018 at NTNU, Norwegian University of Science and Technology. The authors also gratefully acknowledge the case hospital that made it possible to carry out this study.


Yan Li
email: yan.li@gre.ac.uk

Steve Evans

Business model innovation for eco-efficiency: an empirical study

Keywords: Business model innovation, Eco-efficiency, Sustainable business model

. This empirical study conducted 8-in-depth case studies with manufacturing companies across UK and China. The author synthesized the cases and concluded the measures of business model innovation for eco-efficiency in five categories, namely (1) Selling of service model, (2) Direct selling model, (3) Collaboration strategy, (4) Whole system design strategy, and (5) Technology renovation strategy. The empirical finding suggests the adaptation of strategy and exploitation of the technologies are essential to business model innovation when manufacturing companies seeking to implement eco-efficiency.

Introduction

In order to achieve a profitable sustainable business, one possible solution for manufacturing industries is to innovate their current business model to generate more value, as well as increase eco-efficiency to reduce environmental impact [START_REF] Chesbrough | The role of the business model in capturing value from innovation: evidence from Xerox Corporation' s technology spin-off companies[END_REF]. For one thing, an effective business model links the strategic and technical potential, as well as delivering value to customers and generating profits for enterprises [START_REF] Chesbrough | The role of the business model in capturing value from innovation: evidence from Xerox Corporation' s technology spin-off companies[END_REF][START_REF] Lüdeke-Freund | Towards a Conceptual Framework of Business Models for Sustainability, Knowledge Collaboration and Learning for Sustainable Innovation ERSCP-EMSU conference[END_REF]. Additionally, the business model connects the workings inside the firm to outside elements including the customer side, and captures or monetizes value [START_REF] Teece | Business models, business strategy and innovation, Long Range Planning[END_REF][START_REF] Amit | Value creation in E-Business[END_REF]. For another, improving a company's eco-efficiency, enterprises would improve their environmental factor that yields parallel economic benefits [START_REF] Madden | Eco-efficiency: Learning Module[END_REF]. More importantly, eco-efficiency can reduce the environmental impact and natural resources, as well as maintaining or increasing the value of the output [START_REF] Mickwitz | Regional eco-efficiency indicators: a participatory approach[END_REF]. Therefore, linking to business strategies, eco-efficiency plays the role of eliminating risks and identifying new market opportunities, which is very attractive to companies [START_REF] Madden | Eco-efficiency: Learning Module[END_REF]. However, through the literature review, it is apparent that the direct connection of business model and eco-efficiency is deficient, especially for the empirical cases to illustrate how companies adapt their business model to implement ecoefficiency.

The scope of this study focuses on business model innovation and eco-efficiency implementation at organisational level. The author seeks to determine how companies carry out eco-efficiency by means of business model innovation. Thereby, the research question is proposed 'How do manufacturing enterprises innovate their business model to realise eco-efficiency?' To answer the research question, an inductive multiple-case study is conducted. 8-in-depth cases across the UK and China are analysed comprehensively.

2

Literature review

2.1 Business model innovation According to [START_REF] Björkdahl | Editorial: business model innovation -the challenges ahead[END_REF], business model innovation refers to creating or reinventing something new or different in doing business [START_REF] Björkdahl | Editorial: business model innovation -the challenges ahead[END_REF]. In other words, at least one constitution element of the current business model is changed [START_REF] Eurich | A Six-Step Approach to Business Model Innovation[END_REF]. [START_REF] Giesen | Three ways to successfully innovate your business model[END_REF] identified three main types of business model innovation, which are in industry models, in revenue models and in enterprise models [START_REF] Giesen | Three ways to successfully innovate your business model[END_REF]. They proved that each of the three types of business model innovation could lead to success. Among the three types, innovation in enterprise is the most popular, because it focuses on network plays (e.g. external collaborations, partnerships, etc.). At this stage, Velu (2014 p.2) explained that business model innovation can "redefine what a product or service is, how it is provided to the customer, and how it is monetized" [START_REF] Chander | Business model innovation and owner-managers: the moderating role of competition[END_REF]. Therefore, business model innovation can change the mode of competition through altering the performance metrics [START_REF] Daneels | Disruptive technology reconsidered: a critique and research agenda[END_REF].

Foss and Saebi (2016) reviewed 150 publications and concluded that there were four streams of business model innovation [START_REF] Foss | Fifteen Years of Research on Business Model Innovation: How Far Have We Come, and Where Should We Go?[END_REF]. The first stream emphasized the conceptualization of business model innovation [START_REF] Teece | Business models, business strategy and innovation, Long Range Planning[END_REF][START_REF] Amit | Creating Value Through Business Model Innovation Strategy in Changing Markets: New Business Models[END_REF]. This stream offers a clear definition of business model innovation and highlights the phenomenon itself. The second stream argues that business model innovation is an organisational change process and emphasizes the required capabilities [START_REF] Pynnonen | Managing customer-driven business model innovation[END_REF][START_REF] Achtenhagen | Dynamics of business models-strategizing, critical capabilities and activities for sustained value creation[END_REF]. The third stream argues that business model innovation is an outcome of the organisational change process. This stream often regards the emergence of new business in a particular industry such as electric mobility and aviation [START_REF] Foss | Fifteen Years of Research on Business Model Innovation: How Far Have We Come, and Where Should We Go?[END_REF]. The fourth stream addresses the organisational performance implications of business model innovation. This stream differentiates the outcome influenced by the business model innovation process and the performance affected by different types of business model [START_REF] Bock | The effects of culture and structure on strategic flexibility during business model innovation[END_REF][START_REF] Zott | Business model design and the performance of entrepreneurial firms[END_REF].

Eco-efficiency

Eco-efficiency is an essential component of corporate social performance. It also acts as a forward-looking measure of the potential practicality, especially for both researchers and practitioners [START_REF] Guenster | The Economic Value of Corporate Eco-Efficiency[END_REF]. [START_REF] Mickwitz | Regional eco-efficiency indicators: a participatory approach[END_REF] illustrated that eco-efficiency is capable of reducing the environmental impact and natural resources, as well as maintaining or increasing the value of the output [START_REF] Mickwitz | Regional eco-efficiency indicators: a participatory approach[END_REF]. Thus, improving eco-efficiency requires the production of more desirable outputs (GDP), while reducing the consumption of resources and adverse ecological impacts [START_REF] Huang | A comprehensive eco-efficiency model and dynamics of regional eco-efficiency in China[END_REF]. [START_REF] Yu | Eco-efficiency trends in china, 1978-2010: Decoupling environmental pressure from economic growth[END_REF] discussed that eco-efficiency is the main strategy for promoting sustainability through living within the global resource-carrying capacity [START_REF] Yu | Eco-efficiency trends in china, 1978-2010: Decoupling environmental pressure from economic growth[END_REF]. Eco-efficiency is also recognized as a significant tool to evaluate environmental and economic challenges at the same time [START_REF] Remigio | Modelling green and lean supply chains: An eco-efficiency perspective[END_REF]. Specifically, it indicates that a firm displays a good financial performance with less environmental impact or offers a high-quality product with added value [START_REF] Nikolaou | A framework to measure eco-efficiency performance of firms through EMAS reports[END_REF].

Currently, eco-efficiency is widely accepted both for increasing economic value and reducing environmental effects [START_REF] Suh | Eco-efficiency Based on Social Performance and its Relationship with Financial Performance[END_REF]. It is an instrument for sustainability analysis, showing the empirical economic relationship between environmental cost or value and environmental input [START_REF] Mickwitz | Regional eco-efficiency indicators: a participatory approach[END_REF].

Methods

The aim of this research is to explore how do companies realise eco-efficiency by means of business model innovation. There exist two main activities in theory development, which is "the formulation of propositions" and "testing whether they can be supported" [START_REF] Dul | Case Study Methodology in Business Research[END_REF]. This research uses a qualitative case study to explore knowledge inductively [START_REF] Eisehardt | Building theories from case study research[END_REF]. Hence, this research is based on the exploration for theory to discover the strategies, which instructs practitioners identify their possible solutions to implement eco-efficiency effectively.

There are three justifications to select case study as the main research method. Firstly, case study is one of the most flexible methods, which provides a plenty of detailed description of a particular phenomenon [START_REF] Hakin | Research Design: Succesful Designs for Social Economics Research[END_REF]. Secondly, this research focuses on the contemporary event, which is identical to the conditions of case study method [START_REF] Yin | Case Study Research . Design and Methods. 5th edition[END_REF]. Thirdly, According to Eisenhardt (1989) and [START_REF] Hagg | Case studies in Accounting Research[END_REF], many single-case are criticized, as they cannot provide any basis for generalization, while multi-case construct a stronger basis for knowledge building. This research is selected to use multiplecase study [START_REF] Eisehardt | Building theories from case study research[END_REF][START_REF] Hagg | Case studies in Accounting Research[END_REF].

The data collection method of the research is case study. Eight cases include 26 interviews are conducted. Interviewees are CEO, Strategic manager, and engineers. The following Table 1 presents the detailed list of interviews.

Since no previous literature illustrates how companies could innovate an effective business model in the drive towards eco-efficiency, the most suitable structure of this research is the theory building structure. Hence, the theory is built up from the real world and it is data-driven research. To conclude, inductive grounded theory is the most appropriate one for data analysis in this research.

Table 1 List of interviews

Findings and Discussion

Selling of service model Conventionally, companies sell products to customers and encourage customers to buy new products consistently. In this business model, companies upgrade their new generation of products to make customers buy the latest one, although their previous products are still in a good, usable condition. In this context, companies will not pay much attention to the durability of their products. This is because, the more goods they sell, the more profits they make. Moreover, taking the customers' point of view, when they buy a new product, they do not have a proper channel to dispose of their old one. For instance, most customers would like to keep their old usable phones, since the trade-in price is not fair enough for a phone in a good condition. In this sense, customers are not able to recycle their products properly, which is a waste of materials and energy.

In contrast, in the innovated business model, companies retain the ownership of their products. They sell the usage of the product and service to customers. Customers pay the subscription fees or rental fees per month to have the right to obtain a new product when the old one has been used until the end of its life or when the new generation product has been released. This innovated business model encourages companies to increase the durability of their products, and companies naturally manufacture high- quality products to reduce maintenance costs. In addition, customers do not have to bother to dispose of the old product, since the companies will find the best way to recycle or reuse the product to maximize its value.

Com

Direct selling model

Vit and HT's cases suggest that the 'Direct selling' business model can also be shaped to increase eco-efficiency and reduce the waste of resources. Vit changed their business model from distribution route to direct selling. Since there are no distributors or retailers, Vit's current product price is two times less than before. This innovated business model means that Vit distributes furniture directly to customers. In this sense, they can use suppliers' packaging to pack outgoing parts. They can also reuse wooden stillage, which is used to transport aluminium parts, for a period of around 20 years. This business model allows to control the usage of resources. Otherwise, the distributors/retailers' route will inevitable use extra energy and space for storage and expenditure of extra materials for packaging.

Unlike Vit, HT started to use this 'Direct selling' business model since the company was founded. Hence, there is no data to make a comparison between the previous conventional distribution route and the innovated 'Direct selling' route. The interviewees emphasized that 'direct selling' makes their company understand their customers' requirements better. Also, since there is no distributor or retailer, companies can focus on helping suppliers to carry out eco-efficiency.

Collaboration strategy

The fundamental element of this strategy is to clearly recognize the company's status and the limitation of its resource capability. RS is specialized in participating in various projects. The company currently has eight ongoing projects collaborating with universities, research institutions and other companies. Different projects expand the business domain and generate opportunities for RS. Furthermore, their open-minded action allows the company to take the most advantage from other participants. For example, they can get eco-related consulting suggestions from university researchers and other institutes' consultants. They can also learn from other participating companies how they dispose of their waste and increase energy efficiency. CC's data suggests that collaboration helps to increase the public's cognition of eco-efficiency. As a leading glass recycling company in Taiwan, CC has a positive brand value. They recognized that their brand popularity attracts potential collaborators and the collaborators will in turn increase their brand popularity in a positive circle. More importantly, by means of collaboration, the public's awareness of recycling will be increased. Their brand will become more famous and their products will be more easily accepted by customers.

Whole system design

Upgrading the business strategy from the individual level to the 'whole system' level is one of the most eco-efficient measures that a company can take. Conventionally, companies consider individual components in a narrow perspective. The innovated 'whole system' view considers a product's economic and environmental performance throughout its life, which increase the usage from several years to a few decades. Taking the 'whole system' view, companies consider both the short-term and long-term views, and allocate resources (e.g. materials, energy, water) more effectively. For instance, AB changed their business model 'from architect to integrate'; they believe innovation is their key driving force. Hence, considering the whole system with a long vision, AB outsourced the production of several individual components to the OEM company. They concentrated on innovating the technology to design an eco-efficient aircraft. Moreover, HT, as a start-up company, built up a 'helicopter view' to design their electric bicycle. They looked at every single process through an eco-efficiency lens to increase productivity and save energy in production. For example, their folding bicycle design makes logistics easier both for supplier's distribution and customers' portability.

Technology renovation strategy

GXA's data suggests that upgrading technology is a key strategic revolution to improve eco-efficiency. For GXA, as a traditional casting manufacturing company, their traditional technique is to pour molten iron into a cavity with the required component dimensions moulded by sand. This technique produces enormous pollution. The working atmosphere is hazardous for operators. The company developed a 3D printing technique to print components directly, which has replaced the moulding and iron-pouring procedure. This new technique has increased their production efficiency by five times the previous rate. However, since the materials' challenge has not been fully conquered, this 3D printing manufacturing technique is still undergoing development.

Concluding remarks

The empirical research contributes practical implications for professional practitioners in industry. The empirical cases identified the status of the leading eco-efficient companies and their measures to induct eco-efficiency. The author synthesizes the cases and concludes the measures as five categories, namely: (1) Selling of service model, (2) Direct selling model, (3) Collaboration strategy, (4) Whole system design strategy, and (5) Technology renovation strategy. This finding confirmed the existing literature identifying that business model innovation generates value for companies. This study contributes to how adaptation of strategy and business model innovation can be made, when manufacturing companies seek to implement eco-efficiency.

The limitation of this research is the eco-efficiency research mainly based on strategic level. The techniques and measurements to improve eco-efficiency are not covered. In this sense, further research is suggested to extend technical data to the measures of ecoefficiency implementation.