Youngsoo Park

Ilkyeong Moon
email: ikmoon@snu.ac.kr

UAV Set Covering Problem for Emergency Network

Keywords: UAV, Disaster management, Set covering problem

Recent technology allows UAVs to be implemented not only in fields of military, videography, or logistics but also in a social security area, especially for disaster management. UAVs can mount a router and provide a wireless network to the survivors in the network-shadowed area. In this paper, a set covering problem reflecting the characteristics of UAV is defined with a mathematical formulation. An extended formulation and branch-and-price algorithm are proposed for efficient computation. We demonstrated the capability of the proposed algorithm with a computational experiment.

Introduction

Over the last few years, there has been an increasing interest in unmanned aerial vehicles (UAVs) in the various fields including military, telecommunication, and aerial videography [START_REF] Kim | Stochastic facility location model for drones considering uncertain flight distance[END_REF][START_REF] Kim | Traveling Salesman Problem With a Drone Station[END_REF]. Although it has been widely used for commercial or military purposes, this study suggests that UAVs can also be useful for disaster management. When a disaster occurs, activities to mitigate further damages, such as relief logistics, casualty transportation, and evacuation, are planned. Because of extremely varying situations in the demand (disaster) areas, it is crucial to establishing a plan with the scientific decision. Therefore, accurate data collection through contacting with survivors is needed to make these activities efficiently. However, large-scale disasters can cause survivors to be isolated or disconnected in disaster areas. In this case, the reconstruction of the temporary network by using the UAVs can accommodate the communication with the survivors and gathering the real-time data [START_REF] Aida | Rescue Activity for the Great East Japan Earthquake Based on a Website that Extracts Rescue Requests from the Net[END_REF][START_REF] Heinzelman | Crowdsourcing crisis information in disaster-affected Haiti[END_REF]. By using UAVs of built-in network routers, rebuilding the network on shadow areas can be realized when the proper amounts of UAVs are distributed in the appropriate locations. If the number of UAVs is sufficient, launching with a large number of UAVs simultaneously can reconnect the network easily. However, the decision maker with the limited resource is obliged to make an optimal plan because the risk of either under-or-over plan would result in the damage of human life. Therefore, the following two questions can be raised naturally.

• What is the minimum number of UAVs to cover all areas?

• Where should each UAV be located?

By developing the mathematical model based on the set covering problem, the minimum number of UAVs and their flight position to cover every survivor was analyzed in this research.

The proposed UAV set covering problem (USCP) generalizes the classical set covering problem by incorporating the flexible characteristic of UAVs which have no restrictions on the position of facilities that can be located. As well as a disaster situation, USCP can model various environments, including manufacturing industry. In the smart factory, established by industry 4.0, individual resources communicate with each other via wired or wireless network. Especially for the wireless network, it is vital to cover every resource efficiently with minimal investment. As with the USCP, the location where the wireless network router can be installed at this time is relatively free, making it impossible to choose over given candidates of positions.

This study proposes a branch-and-price approach to overcome the intractability caused by the quadratic constraint and solve USCP for efficient computation. The overall structure of the study takes the form of 5 chapters, including this introductory chapter. Section 2 is concerned with the description of USCP and the standard formulation of the mathematical model. In Section 3, an extensive formulation and a branch-and-price algorithm are presented for the problem. In Section 4, computational experiments are conducted, and results are analyzed. Section 5 summarizes the findings of the research.

Problem Definition and Mathematical Formulation

The objective of USCP is to cover every demand point with the minimum number of UAV with a fixed coverage and without a restrictions on the position. The detailed assumptions of USCP are defined as follows: (1) Positions of demand points are deterministic. (2) Coverage distance of UAV is identical. (3) There are no restrictions on the position of UAV. ( 4) Each wireless network is uncapacitated and the network traffic is ignored. [START_REF] Vanderbeck | Reformulation and decomposition of integer programs[END_REF] Overlap interference between UAVs and shadowing effect by buildings are ignored.

A mathematical model is developed based on the assumptions. Let N denote the set of the demand points where the survivors are distributed. The following is the notations used in the standard formulation for USCP.

Parameters a x

i a position of demand point i on x-coordinate. ∀i ∈ N a y i a position of demand point i on y-coordinate. ∀i ∈ N R coverage radius of a UAV.

Decision variables

y j = 1, if UAV j is used. 0, otherwise. ∀j ∈ N x ij = 1, if demand point i is covered by UAV j. 0, otherwise. ∀i ∈ N ∀j ∈ N c x j ∈ R, position of UAV j on x-coordinate. ∀j ∈ N c y j ∈ R, position of UAV j on y-coordinate. ∀j ∈ N
When the coverage range is given as a parameter, what we are interested in is the minimum number of UAVs and the position of each UAV in the x-y plane to cover all demand points. The relevant mathematical formulation based on mixed-integer programming is developed as follows:

min m j=1 y j (1)
s.t. x ij ≤ y j , ∀i ∈ N, ∀j ∈ N (2) m j=1 x ij ≥ 1, ∀i ∈ N (3) (a x i -c x j ) 2 + (a y i -c y j ) 2 ≤ R 2 + M (1 -x ij ), ∀i ∈ N, ∀j ∈ N (4)
x ij ∈ {0, 1}, ∀i ∈ N, ∀j ∈ N (5)

y j ∈ {0, 1}, ∀j ∈ N (6) c x j , c y j ∈ R, ∀j ∈ N (7)
Objective function (1) minimizes the number of UAVs to cover all demand points. Constraint [START_REF] Kim | Traveling Salesman Problem With a Drone Station[END_REF] indicates the linking constraint between a demand point and a UAV. That is, a UAV j is required to cover a demand point i. Constraint (3) represents that each demand point i should be covered by one UAV. Constraint (4) is the logical constraint to incorporate the network coverage of the UAV. When the demand point i is covered by the UAV j, the location of the demand point (a x i , a y i ) is covered by a circle with a circumcenter placed on the point (c x i , c y i ). Constraints ( 5) and ( 6) mean that variables x ij and y j are binary variables. Constraint [START_REF] Osman | Capacitated clustering problems by hybrid simulated annealing and tabu search[END_REF] means that c x i and c y i are the non-negative real variables. For distinction, the formulation will be renamed as Euclidean standard formulation (ES). ES contains the non-linear constraint as presented in Constraint (4). Accordingly, it is hard to obtain the optimal solution within a reasonable time, even for small-sized problems. Since the fast decision is vital in the response for disaster management, a branch-and-price approach for USCP is designed, which will be introduced in the next section.

3 Branch-and-Price Approach for USCP Branch-and-price (B&P) approach is a well-known exact-algorithm for largescale optimization problems. By incorporating the column generation technique into the branch-and-bound, it can significantly improve the bounds of the linear programming relaxation and resolve the symmetry of the solutions while branching. For detailed information of the B&P approach, one can refer [START_REF] Vanderbeck | Reformulation and decomposition of integer programs[END_REF].

Master problem

Denote by Ω is the set of the possible patterns to cover the demand points by one UAV. The patterns are defined with a given parameter w ij indicating the inclusion of each demand point for a pattern. The minimum number of UAVs can be determined by an integer program as follows:

min j∈Ω y j (8) s.t. j∈Ω w ij y j ≥ 1 ∀i ∈ N (9)
y j ∈ {0, 1}, ∀j ∈ Ω (10)
Objective function (8) minimizes the number of UAVs required to cover all demand points. Constraint (9) is related to an assignment constraint to the demand points. The optimality under the current basis is determined by the pricing subproblem.

Pricing subproblem

We have defined π i as the dual price of constraint (9). By solving the pricing subproblem, one can identify whether there is a better assignment pattern of demand points for a UAV. To construct a pattern (column), the decision variable is binary to identify whether a demand point i is covered by the generated column or not. Another decision variables c x and c y represent the position of UAV of the generated pattern. Additional columns for the master problem is generated by solving the following pricing problem:

min 1 - m i=1 π i w i (11) s.t. (a x i -c x ) 2 + (a y i -c y ) 2 ≤ R 2 + M (1 -w i ), ∀i ∈ N ( 12
)
w i ∈ {0, 1}, ∀i ∈ N (13) c x , c y ∈ R, (14)

Computational Experiments

To compare the effectiveness of the proposed solution algorithms, computational experiments are performed. All optimization models were developed in FICO Xpress Mosel version 7.9. Experiments were performed with Intel R Core TM i5-6600 CPU @ 3.30GHz and 32 GB of RAM operated on Windows 10 64 bit OS. To be applied in the disaster management, each experiment was conducted with the run-time limit of 1800 seconds. Data set was made based on the benchmark data from OR-Library [START_REF] Beasley | OR-Library: Distributing Test Problems by Electronic Mail[END_REF][START_REF] Osman | Capacitated clustering problems by hybrid simulated annealing and tabu search[END_REF]. For each size of demand points of 10, 20, and 50, 10 instances were created, and the demand points were distributed uniformly on the 100 x 100 Euclidean plane. Three coverage radiuses of 10, 20, and 30 were examined for each instance. An analysis of algorithmic performance and sensitivity analysis are provided for the managerial insight in disaster management. Table 1 lists the computational results. The columns in this table are defined as follows. #Opt/#F eas: the number of solved/feasible-solution-provided problems within the time limits. T ime: the average of the computation time to solve the problems. For the problems not solved within the time limit, we used 1800 seconds while calculating the average. Gap L : the average of the gap between lower (LP) bound and the feasible solution. # of UAVs: the average of the objective value of the feasible solution. Gap: the average of the gap between # of UAVs of ES and B&P algorithm, calculated by {(# of UAVs of ES)-(# of UAVs of B&P)}/(# of UAVs). As shown in Table 1, ES was not capable of providing optimal solutions even for the smallest problems. Long computation time and high Gap L of ES were caused by both factors of weak LP bound and scarce feasible solution. ES could not provide feasible solutions for 49 of 90 problems and it led to the high average of the number of UAVs required to cover the demand points. Especially for the problems with the 50 demand points, Gap L were higher than 88% and showed the intractability of ES. B&P algorithm solved 76 of 90 problems and provided a feasible solution for every problems. Thus, for 7 of 9 classes of problems showed 0 for Gap L . For every data set used for the computational experiment, Gap was always the same or less than zero, which meant that B&P algorithm makes a better plan to use fewer UAVs to cover the area.

Certainly, there was a tendency that the smaller the radius of the coverage be, the more UAVs were required. For the same coverage with the different number of demand points, the number of UAVs grew with the number of demand points, too. However, under the fixed-size area, the growth rate was less than 1. In other words, 7.2 UAVs with coverage radius 10 were required to cover 10 demand points, but only 10.7 UAVs were required to cover 20 demand points. In extreme cases, there is an upper bound of the number of UAVs, which will cover the whole area without any network-shadow area.

Conclusions

We introduced a UAV set covering problem with fixed coverage and without restrictions of positions for planning an emergency wireless network in disaster areas efficiently. Due to the intractability of quadratic constraints, proposed ES could not provide an optimal solution by a commercial solver within a practical time. An extended formulation of ES was proposed to implement B&P algorithm for USCP, which provided a better LP-bound and removed the symmetry of the solution. The computational experiment showed that B&P algorithm can provide an optimal solution for small-sized problems within reasonable time limits. Sensitivity analysis was conducted to show the tendency between the number of demand points, radius, and the number of UAVs required.

Table 1 .

 1 Computational results

	ND Rd	#Opt/#F eas T ime(s)		GapL(%)	# of UAVs Gap(%)
			ES	B&P	ES	B&P	ES	B&P	ES	B&P B&P
	10	10	1/9 10/10 1661.2 0.2	33.32 -	7.6	7.2	-4.43
		20	9/9 10/10 388.8 1.1	4.00	-	4.6	4.0	-6.00
		30	10/10 10/10 11.2	2.7	-	-	2.8	2.8	-
	20	10	0/0 10/10 1800* 17.9	85.00 -	20.0 10.7	-46.50
		20	0/9 10/10 1800* 91.9	46.00 -	6.9	5.5	-7.00
		30	5/10 10/10 944.8 78.3	12.50 -	3.5	3.5	-
	50	10	0/0 10/10 1800* 447.9	96.00 -	50.0 16.5	-67.00
		20	0/0	5/10 1800* 1594.7	96.00 26.77	50.0 11.6	-85.56
		30	0/2	1/10 1800* 1765.1	88.47 68.61	41.0 17.8	-75.64
	1800*: No problem is finished within the time limits.

Acknowledgements

This research was supported by the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning [Grant no. 2017R1A2B2007812].

This research was supported by the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning [Grant no. 2017R1A2B2007812].


Takeshi Kurata
email: kurata-takeshi@sei.co.jp

Takashi Maehata

Hidehiko Hashimoto

Naohiro Tada

Ryosuke Ichikari

Hideki Aso

Yoshinori Ito

IoH Technologies into Indoor Manufacturing Sites

Keywords: IoH, Indoor positioning, Work motion recognition, 6M, Mieruka, Kaizen, Manufacturing site

This paper focuses on introducing measurement technologies into manufacturing sites regarding the worker-oriented part of 6M, which consists of Man, Machine, Material, Method, Mother Nature, and Money. First, we introduce indoor positioning and work motion recognition systems that we have developed as key components of Internet of Humans (IoH) technologies. Next, we briefly report on two case examples of manufacturing sites where worker behavior measurement, analysis, and visualization are promoted. Then, we conclude this paper with discussion about the costs and benefits on the introduction of indoor positioning technologies into manufacturing sites.

Introduction

To comprehensively understand and specifically improve the situation of manufacturing sites, it should be effective to aggregate big data regarding 6M (Man, Machine, Material, Method, Mother Nature, and Money) [START_REF] Kurata | Towards Realization of 6M Visualization in Manufacturing Sites[END_REF]. This research is currently continuing with the aims of realizing 6M 'mieruka', which means visualization or vision control, as well as providing technologies which support continual kaizen and work/safety/health-care management. With the proliferation of Internet of Things (IoT) products and services, visualization in terms of tangible things (Machine and Material) such as facilities, equipment, raw materials (RM), finished goods (FG), etc. is progressing rapidly for grasping the present situation and confirming the result of kaizen (improvement). However, the development of visualization technologies and methodologies is still ongoing when it comes to intangible things (Man, Method) such as worker conditions and workflow processes [START_REF] Fukuhara | Improving service processes based on visualization of human behavior and POS data: a case study in a Japanese restaurant[END_REF]. The lack of relevant data on human behavior could be considered a major disincentive for progress on this front. It is relatively easy to collect individual worker data in cases where the work is repetitive in a specific area of mass production. In cellular manufacturing or high-mix low-volume production, however, moving and working are often combined, and it poses a major barrier to data collection on each worker. Therefore, it is crucial to further develop and utilize Internet of Humans (IoH) technology for collecting data on human behavior. As there is often a high correlation between worker positions and operation contents, and also manufacturing sites are mainly occupied by indoor environments, indoor positioning technology is regarded as one of key IoH components.

Accordingly, this paper especially focuses on measurement technologies of the worker-oriented part of 6M, and briefly reports on two case examples of manufacturing sites where worker behavior measurement, analysis, and visualization are promoted. It also discusses the costs and benefits of the introduction of indoor positioning technologies into manufacturing sites.

2

IoH Technologies

Indoor Positioning System Configuration

Our indoor positioning system shown in Fig. 1-left has two features suitable for introducing into actual manufacturing/service sites. The first feature is that the system utilizes xDR (Dead Reckoning for X) which includes PDR (Pedestrian Dead Reckoning) [START_REF] Kourogi | Personal positioning based on walking locomotion analysis with self-contained sensors and a wearable camera[END_REF] and VDR (Vehicle Dead Reckoning) [START_REF] Ichikari | Off-Site Indoor Localization Competitions Based on Measured Data in a Warehouse[END_REF] as a method of relative positioning. The second feature is that solar-powered BLE beacons are employed as stationary nodes placed around the area of interest. This system is an integrated positioning system that consists of xDR, RSSI-based BLE positioning, and map constraints.

Although solar-powered BLE beacons are twice to three times as expensive as typical battery-powered BLE beacons, the combination of xDR and BLE positioning makes it possible to reduce the number of BLE beacons to only a fraction of the number it would take with only BLE positioning. This allows for a battery-free setup without increase in initial installation costs. Additionally, a battery-free setup eliminates the need for battery replacement, greatly reducing operational costs. Although the configuration inherently has issues on lighting for energy harvesting, we have been developing a method of sustain positioning performance with uneven time interval of BLE signal transmission due to insufficient lighting. We will present the details in near future.

Stationary nodes are naturally placed for covering areas of interest. However, significant flow lines or activities may occur outside of the expected areas of interest, and there are cases in which such data are crucial for worker behavior analysis. Although there is nothing to do with only BLE positioning, we still can keep tracing flow lines with xDR.

As hardware for mobile nodes, the workers are able to use embedded modules and smartphones equipped with a ten-axis sensor (3 for acceleration, 3 for angular velocity, 3 for magnetism, 1 for barometric pressure). Especially because of their increasing size in recent years, smartphones were not used in this research as they could impede worker operations. Instead, the Android-based terminal BL-02 made by BIGLOBE is adopted in our manufacturing sites. The same setup can be also applied to forklifts for VDR.

Fig. 1-right shows the cost-effective arrangements of stationary and mobile nodes. In general, transmitters such as BLE beacons and passive RFID tags are much less expensive than receivers or transponders such as smartphones and IoT gateways. In cases with a larger area and fewer number of persons measured, it is better to choose transmitters as stationary nodes and receivers as mobile nodes (SB-type system) from the aspect of cost. Our manufacturing sites are categorized in this case. In addition, because cameras are often difficult to bring in and install on site due to issues of cost and privacy, vision-based positioning technologies are not integrated in our system.

Work Motion Recognition

If micro-level understanding of behaviors is required as in the analysis of hospitality in customer service, cooking and assembly work, skills involved, etc., the use of only position data are not enough for such purposes. Inertia Measurement Units (IMUs) as in mobile nodes for xDR go beyond tracking position, as they are also capable of capturing type and size of motions, allowing for micro-level analysis on work motions and for safety management by detecting falling movements. Fig. 2 depicts three examples of work-motion capture systems. Typically, 10 to 20 IMUs are attached all over the body as in the left example in Fig. 2. Although this sort of setup is usually permitted for short-term collection of data, the time involved in attaching and detaching the system, and its potential to interfere with work tasks make such a system unlikely to be adopted for long-term, everyday use. The system in Fig. 2-center is designed to reduce the number of IMUs to only five, in order to be less cumbersome for workers and reduce hardware costs. In this case, a smartphone is placed inside the 'obi' belt as one of IMUs and also as a BLE receiver.

Compared to a configuration where IMUs are attached to the whole body, this kind of configuration results in a precision reduction of around 10 to 20%. The whole-body configuration provides the position and movement of each body part based on a skeleton model. In contrast, the partial body configuration, as in Fig. 2-center, means some more detailed information on work motions are missed, and we must rely on the local movement data for the available sensors only.

To address such problems, an integrated IoH sensor module with a wearable passive RFID tag reader and a ten-axis sensor module have been developed through METI's project to support the advancement of strategic core technologies (represented by Gobi) (Fig. 2-right). This allows for the micro-level information lost in the decreased number of IMUs mentioned above to be partially obtainable once more by taking micro-positional data with RFID tag reading, and improvement in motion recognition precision can be expected.

Manufacturing Site Case Studies

In this section, we briefly introduce two sample cases of manufacturing sites where worker behavior measurement, analysis, and visualization are promoted. Implications and suggestions through the following cases are not well structured, however, we believe that they fit properly to many cases at actual manufacturing sites, and therefore they would be helpful for a person in charge to develop or install systems for worker behavior measurement, analysis, and visualization.

Cable Manufacturing Line

J-Power Systems Co., Ltd. (hereafter referred to as JPS) is a member of Sumitomo Electric Group, and is involved in the designing, manufacturing, constructing, and selling of electric power transmission cables, overhead power transmission lines, amongst other things. We are introducing the indoor positioning system depicted in Fig. 1-left and the 6M visualization setup in hopes of increasing equipment utilization rates on cable production lines.

Although not addressed much in this paper, the first step of implementation at JPS involved the Mother-Nature (Environment) aspect of 6M. A 3D model of the indoor environment was automatically generated using LIDAR and an omnidirectional camera [START_REF] Kuramachi | G-ICP SLAM: An odometry-free 3D mapping system with robust 6DoF pose estimation[END_REF]. This 3D model was then converted into a 2D floor map for the site's area of interest. As shown in Fig. 3-left, BLE beacons were placed on the floor map and the beacons' geospatial coordinates on a real-world coordinate system were automatically extracted. Floor maps are necessary for positioning programs and visualization tools, but many sites lack a CAD model or otherwise, have CAD models with outdated information. Furthermore, the process of calculating geospatial coordinates for BLE beacons can be made complicated by inaccurate floor maps. The approach we have taken makes it possible to avoid such issues.

The installation of a safety laser rangefinder (LRF) has also been arranged for preventing accidents in which workers are caught in equipment. Within the range of measurement, the LRF can be used for precise positioning. Another precise position correction by the PLC also becomes possible. However, since the worker ID is unknown in both methods, it is necessary to have a process to assign IDs with PDR.

Assembly Line

At an assembly line in a factory of Company A, motion capture data have been gathered by multiple IMUs attached to each part of workers' bodies, and micro motion recognition on the assembly line is underway. From this, we hope to precisely make standardized procedure for work element, and efficiently evaluate the variation in work involved.

On typical assembly lines, the work of interest (Material) often moves a little bit at a time. Because of this, it is not enough to simply obtain a static floor map, as was the case at JPS. Rather, linking dynamic data on the line with sensor data makes it possible to perform highly accurate work motion recognition. In addition, quirks in movement peculiar to each individual are inevitably included in the machine learning training data for micro-motion recognition. There is a need for development of technologies and methodologies to realize not only efficient training data collection but also motion recognition which is not affected by such individual differences.

Discussion

Make Time Tangible

We would like to discuss some of the things that should be considered when introducing measurement technologies to manufacturing or service sites. Generally, there is little objection to the importance of visualization when it comes to 6M as a whole or in part. However, thinking that short-term data collection and a one-time "before and after" comparison is sufficient, or that using a single system successively in many different areas can cut costs, rather than opting for continual long-term data collection, are common lines of thinking.

To shed light on these misconceptions, this section uses Fig. 4 to discuss some of the advantages of continual on-site measurement of data. The top and middle portions of the diagram show sites that are not continually collecting data, while the bottom shows a site that is. Firstly, a common concern heard during on-site interviews is that workers are not used to their work being monitored, and it will be difficult to determine whether the resulting data reflect realistic and natural circumstances or not. Continual data collection would allow such uncertainty to be dispelled.

It is necessary to consider how causes of found issues are analyzed and addressed. Typically (i.e., where continual data collection has not been adopted), the data collection system is only set up after a problem is identified, in which case one must wait for enough data surrounding the issue to be collected (a period that is referred to as the "Before data collection"). This results in a significant time lag between when the problem is first identified and when its causes can begin to be analyzed and addressed. Furthermore, as shown in Fig. 4-top, if the exact issue of concern does not arise once again after beginning Before data collection, there is no data to analyze to identify causes.

Dashboard cameras in cars use accelerometers to detect incidents such as collisions and sudden braking, and are able to keep records of such incidents for future use and, report accidents. Likewise, with continual data collection in manufacturing or service sites, issues can be analyzed at any time, with seamless transition into data collection for confirming the effectiveness of solutions (the "After data collection"). In this way, issues can be swiftly addressed and dealt with. This "Virtual Time Machine" concept [START_REF] Hirose | Virtual time machine[END_REF] is expected to take root in more areas in the future as 6M visualization technology continues to develop and mature.

Cost Comparison

With regard to the costs involved in introducing human behavior measurement technology, in this research, we considered three-company cases, which are Companies P, Q, and R, to investigate the costs of time study and work sampling in human-wave tactics, as well as of automatic data collection with data collection systems. The details for each case are as follows:

• Company P: Time study by video recording, 1 observer, 1 worker recorded, and macro-positioning granularity [START_REF] Kurata | Towards Realization of 6M Visualization in Manufacturing Sites[END_REF] (TS-Macro)

• Company Q: Work sampling, 5 observers, 33 workers recorded, and mezzo-positioning granularity (WS-Mezzo)

• Company R: Time study by video recording, 2 observers, 3 workers recorded, and micro-positioning granularity (TS-Micro)

We added one more system configuration for cost comparison. Up to now, each system for the flow-line measurement and analysis discussed here (system #1), for safety management with worker fall detection (system #2), and for health-care management with gait evaluation (system #3) has been individually implemented and adopted at each manufacturing or service site. Because the data collection system, which contains sensors such as accelerometers, used in system #1 can also be used for systems #2 and #3, it is possible for the systems to integrate with one another. If the system cost can be evenly distributed across those three systems #1, #2, and #3, the cost of system #1 can be reduced to a third.

The results of cost comparison are shown in Tab. 1. In all of three companies' cases, we confirmed that the costs of time study and work sampling in human-wave tactics gets higher than the ones of data collection systems usage.

Additionally, in the case of Company Q, because work sampling was conducted, there is inevitable loss of data in human-wave tactics, whereas visualization was possible with the lossless data by adopting automatic measurement. Integrating work analysis with health-care and safety management into a unified system brings additional value and advantages beyond just system cost reduction. For example, it is possible for such a system to send a fall-detection-based emergency call with position information even indoors although conventional fall detection systems do not cover indoor sites.

Table 1. Cost comparison of manual observation and automatic measurement with three cases.

H: Cost for human-wave tactics, S: Cost of data collection system usage, D: 1/3 cost of data collection system usage.

Conclusion

Needless to say, even if massive amount of 6M data can be gathered by IoT/IoH devices in real manufacturing sites, it does not hold all the answers to comprehensively understand the real sites since big data in general has issues on quality and variety. In-depth surveying such as retrospective interviewing has potential to complement the defect of big data, however, it inevitably requires intensive effort with high work load. In-depth surveying with subject screening based on big data would alleviate the load, and it would result in efficient surveying with both breadth and depth [START_REF] Nakajima | Cognitive chrono-ethnography lite, work: A Journal of Prevention, Assessment and Rehabilitation[END_REF]. This is consistent with the idea of "Pier Data" outlined in Fig. 6 in which "Big Data" are integrated with "Deep Data" [START_REF] Kurata | Making pier data broader and deeper: PDR challenge and virtual mapping party[END_REF]. Demonstration of such a methodology in actual sites through further development of 6M data collection and visualization technology including IoH technologies is one of our future works.

Fig. 1 .

 1 Fig. 1. (Left) indoor positioning system in use at manufacturing sites; (right) cost-effective arrangements of transmitters and receivers.

Fig. 2 .

 2 Fig. 2. Work motion recognition systems the difference of which is the number of IMUs.

Fig. 3 .

 3 Fig. 3. Example of flow line measurement at a manufacturing site.

Fig. 4 .

 4 Fig. 4. Benefits of continual data collection.

Fig. 5 .

 5 Fig. 5. Screening with 6M big data for obtaining deep data by in-depth survey.

Acknowledgements

This work was supported by JST-OPERA Program Grant No. JPMJOP1612, Japan.


Sergio Gallego-García

Manuel García-García

Design and Simulation of an Integrated Model for Organisational Sustainability applying the Viable System Model and System Dynamics

Keywords: Sustainability, Organisational Management, Supply Chain, Key Performance Indicators, Viable System Model, Simulation, System Dynamics

The current global situation increases the exposure of organisations to their environment. As a consequence, companies have to consider a variety of topics that they had managed to a limited extent before. An organisation should now go beyond its limits in order to maintain its viability over time, this means being sustainable and making its related environment better by enabling collaborative working. In addition, there are not only challenges in the management of external relationships but in the management of internal ones. These are a consequence of the pressure for decision-making in short periods of time and a lack of coordination between different parts of organisations, effectively operating as silos with divergent goals. Even with common goals, due to a lack of adaptability or flexibility, some areas do not see the benefits of coordinating work for securing a long-term USP (Unique Selling Proposition) of an organisation. In this context, for both external and internal challenges, researchers often fail to holistically consider an organisation as being an agent, interacting and creating ecosystems of cooperation. Thus, the aim of this study is to propose a holistic approach as to how organisations can interact with their environment, as well as internally. Hereby, the Viable System Model and System Dynamics were applied. In conclusion this proposed approach enables companies to interact within its area of influence with an efficient approach. In order to prove the conceptual model, a simulation for a manufacturing supply chain and its area of influence was performed.

Introduction

Many companies have "separate kingdoms" in their business operations, with employees being more loyal to their specific business area than to the company itself and the challenge goes beyond the supply chain. As a result, for a company to be successful, a climate of trust, respect and dedication has to be developed by allowing other entities to have their fair share of mutual activities, enabling win-win situations [START_REF] Van Marrewijk | Concepts and definitions of CSR and corporate sustainability: Between agency and communion[END_REF].

Nowadays, our society requires the collaboration of all entities to support its commitment to the community. This means fulfilling human needs by creating a sustaina-ble environment for all human beings. In this context, a company should contribute in its area of influence. However, the company will only support theses purposes if there is a benefit for its actions. Within a company and the society individuals play a significant role. Moreover these individuals needs have needs are classified according to Maslow´s Hierarchy of needs and own interests.

For these reasons, the purpose of this article is to develop a conceptual model for organisational sustainability at three levels: the company itself (with its functional areas as entities), its supply chain and the society in the area of influence of the company.

The initial hypothesis is that an organisation that is built on the basis of sustainable principles and collaborative working using the structure of the VSM will have a positive impact on the achievement of short, medium and long-term goals of all entities; society, supply chain and company goals.

Based on the conceptual model, a case study for an organisation was chosen. The case study was performed for an automotive plant with different suppliers and customers and also including the interaction with society.

Fundamental Definitions and State of Research

Sustainability

The meaning of sustainability depends on the perspective (social, economic or ecological) and the development of appropriate indicators provides a useful framework for policy making [START_REF] Brown | Global sustainability: toward definition[END_REF].

The concept of sustainability assumes that humans and their economic systems are linked [START_REF] Caradonna | Sustainability: A history[END_REF]. The World Commission on Environment and Development defines 'Sustainable Development' as being the "development that meets the needs of the present without compromising the ability of future generations to meet their own needs". As a consequence, sustainability has to not only meet our needs but, also, it has to provide a benefit to society and external systems [START_REF] Haan | The needs of society: A new understanding of transitions, sustainability and liveability[END_REF].

To sum up, the study uses a concept of sustainability that is based on the cybernetic concept of viability. If a company wants to be organised to be able to maintain sustainability, then it needs to be organised with a view to viability [START_REF] Schwaninger | Systemic design for sustainability[END_REF].

The Company and its Organisational Management

The organisational structure of many companies is highly function-driven. This approach to process improvement aims to minimise costs for all of the individual processes and this is a significant mistake because the optimisation of individual processes does not give the optimum for the entire process [START_REF] Kletti | Die perfekte Produktion[END_REF], this has a consequence for the organisation structure.

As an example, the logistical corporate conflict of goals comprises three conflicts of interest [START_REF] Schuh | Logistikmanagement Springer[END_REF]; the conflict between costs and delivery, the conflict between costs and throughput as well as the conflict between throughput and delivery.

2.3

The Company and its Supply Chain (SC): from the Bullwhip Effect to a Collaborative SC

The supply chain components involve all parties, either directly or indirectly, when fulfilling a customer's request and include the manufacturer, suppliers, transporters, warehouses, retailers, and even the customers themselves [START_REF] Chopra | Supply chain management. strategy, planning & operation[END_REF]. A challenge in the supply chain arises from the fact that every stage in the supply chain wants to reach the same goals such us low stocks, high capacity utilisation, high delivery service, etc. creating an unavoidable conflict of interest between the entities.

Moreover, another challenge is created by the lack of transparency between stages, named the 'bullwhip effect'. As a result, demand along the supply chain is difficult to forecast [START_REF] Campuzano | Supply chain simulation: A system dynamics approach for improving performance[END_REF] and it causes an extreme fluctuation of stock [START_REF] Schönsleben | Integrales Logistikmanagement: operations und supply chain management innerhalb des Unternehmens und unternehmensübergreifend[END_REF]. Concepts for a collaborative supply chain strive to mitigate the bullwhip effect through close cooperation between chain members to effectively match the demand and supply that contributes to the increase of the overall chain profitability. Although collaboration is based on a mutual objective, collaboration is a self-interested process in which firms only participate if it contributes to their own survival [START_REF] Simatupang | The collaborative supply chain[END_REF].

Individual and Society Needs in the Area of Influence of an Organisation

Social structures are the result of man's actions in society and these structures enable him to satisfy his needs [START_REF] Haan | The needs of society: A new understanding of transitions, sustainability and liveability[END_REF]. In this study, the definition of human needs by Maslow is used. Maslow studied the motivation of individuals without considering rewards or unconscious desires [START_REF] Mcleod | Maslow's hierarchy of needs[END_REF]. Maslow stated that people are motivated to achieve certain needs. When one need is fulfilled a person seeks to fulfil the next one, and so on [START_REF] Maslow | A theory of human motivation[END_REF]. The analysis of Maslow´s hierarchy within organisational studies remains "extremely rare" and therefore it has a high potential [START_REF] Dye | Maslow: Man interrupted: Reading management theory in context[END_REF]. The hierarchy of needs can be divided into 3 types of needs: basic needs, psychological needs and selffulfilment needs [START_REF] Poston | Maslow's hierarchy of needs[END_REF]. There are 5 categories: physiological needs, safety and security needs, love and belonging needs, self-esteem needs and self-actualisation needs [START_REF] Maslow | Maslow's hierarchy of needs[END_REF].

Concurrent and Collaborative Work Applied to an Organisation and its Related Environment

Organisational behaviour is described as being the result of concurrent actions of the "intelligent adaptive agents", i.e. the individuals within an organisation. Therefore, organisational performance is a function of both individual actions and the context or environment in which they act [START_REF]Handbook of systems engineering and management[END_REF]. As a consequence, the principles of concurrent and collaborative work are used as the basis for the conceptual model development. Therefore, a model in which coordination mechanisms and tools for sharing information are given is compared with a model in which those tools do not exist.

Methodology and Concept Development

The method used to reach this goal was the following:

1. Methodological approach: the VSM was selected due to its structure for viability and SD due its capability to analyse the behaviour of complex systems over time 2. Literature review 3. Conceptual model design: logical inter-relationships and casual loop diagrams 4. Simulation and analysis of results: simulation models, assumptions and scenario definitions, extraction and analysis of results

Critical reflection and outlook

Beer deduced the VSM by taking the central nervous system of the human being and cybernetics as basis in order to deal with complex systems. The VSM is built on three main principles: viability, recursivity and autonomy [START_REF] Schuh | High resolution supply chain management: optimized processes based on self-optimizing control loops and real time data[END_REF]. The cybernetic model of every viable system consist always in a structure with five necessary and sufficient subsystems that are in relation in any organism or organization that is able to conserve its identity with independency of its environment [START_REF]The viable system model: interpretations and applications of Stafford Beer's VSM[END_REF]. As described in the literature the VSM is a conceptual and methodological tool for the modelling and design of organisations and its areas with the goal of being viable [START_REF] Schwaninger | Intelligent organisations: Powerful models for systemic management[END_REF]. Moreover, SD is a computer-guided approach for studying, managing and solving complex feedback problems with a focus on policy analysis and design [START_REF] Angerhofer | System dynamics modelling in supply chain management: research review[END_REF]. It is increasingly used to design successful policies in companies and public policy settings [START_REF] Sterman | Business dynamics: systems thinking and modeling for a complex world[END_REF]. For this reason, Vensim (an SD software) was used to program the conceptual model for the case study.

4

Design and simulation of an Integrated Model for Organisational Sustainability: Applying the Viable System Model and System Dynamics

Design of an Integrated Model for Organisational Sustainability: Applying the Viable System Model

The three levels under study are: society, the supply chain and the company itself. As applying the VSM all levels consist of an invariant structure of 5 subsystems: normative (system 5), strategic (system 4), tactical (system 3), coordination (system 2) and operative units (system 1). In this research study the supply chain is the network level (recursion level n), the company is the plant level (recursion level n+1) and society is the related environment for the company.

For the company, the key indicators for every functional area should be described in order to understand the motivation of each entity inside the organisation, systems 1, i.e. the operative units within the recursion level n+1. Then, the collaborative and mutual benefits for the functional areas are searched for, thanks to a system dynamics analysis with casual loop diagrams:

Fig. 1. Extract of a company casual loop diagram: interrelationships between key indicators

This was repeated between all indicators of the above-listed functional areas. As a second step, the recursion level of the supply chain was considered. For this, the different entities, their objectives and key indicators were considered in order to determine potentials for collaboration across the supply chain. In the same way as for the company recursion level, casual loop diagrams were developed for the participants in the supply chain describing the inter-relationships between key indicators. As an example, if the retailer knows the real demand information, then the logistics routes and the utilisation rates of trucks can be optimised, thereby reducing the distribution costs and improving the customer order lead time. By using this approach, benefit-sharing can be achieved.

In the third level, society and its needs in the area of influence of the company are considered. For this, only the basic needs of society are taken into account. The following needs, indicators and measures that are contributed from the company to society were considered in the conceptual model and in the simulation.

Simulation of an Integrated Model for Organisational Sustainability using System Dynamics

The previously described levels were programmed into the Vensim software. For the case study, an automotive plant was selected. Several suppliers and a retailer with end-customers were considered. The assumptions in the simulation were: one car model, 5 year simulation time with 220 working days, a maximum capacity and demand of 1,000 cars per day: From the results obtained, it can be observed how the collaborative work simulation model presents better results for all three levels.

Critical Reflection and Outlook

After completion of the research work, the following points can be successfully concluded:

 Thanks to a new model for organisational management that is able to make decisions in relation to its environment, its supply chain partners and internal set-up, the sustainability of an organization and its related environment can be assured.  The VSM provides the necessary structure to determine the inter-relationships between areas and parameters that allow them to be optimised in a recursive way.  The simulation of an organisation using the conceptual model with collaborative mechanisms presents better results for the relevant key parameters for all three levels of all entities: company, supply chain and society impact. As a result, the simulation evolves in a decision-making support tool for managers.

The final goal is to transfer this research method to real organisations, applying it in particular cases as a management guide for sustainable design and development. Moreover, future research would be to study the specific levels in detail before trying to implement it in a real organisation. In summary, all individual needs and society needs can be included as demand factors to complete the model.

Table 1 .

 1 Key Indicators for functional areas of an organisation

	Functional area	Indicators
	Production	Production volume, performance rate, production costs, production lead time, OEE, capacity utilisation rate
	Quality	Quality or rejection rate, control and rework costs
	Maintenance	Availability, maintenance costs
	Logistics	Service level, customer order lead time, logistics costs
	Design & development	Time-to-concept, number of variants, variants adding value to end-customer
	Sales & Marketing	Customer demand, brand image, employer image, price, margin
	Purchase	Number of suppliers, material costs, time to negotiate

Finance & IT Product development costs, investments, logistics, production, maintenance, quality & general costs, cost per unit, margin per unit Human resources Number of employees, training, motivation, conditions Other staff areas Legal, compliance, strategy, etc.

Table 2 .

 2 Key Indicators for the supply chain participants

	Participants Indicators
		Replenishment time, rejection rate from suppliers, raw material stock levels,
	Company	capacity utilisation, finished product stock levels, customer order lead time, service level, production costs, level of exchange of real customer demand
		information, adaptability to change
	Suppliers	Stock level or capital employed, capacity utilisation, delivery service, product quality, order lead time, level of exchange of real customer demand information

Table 3 .

 3 Key Indicators & potential measures for basic needs in the area of influence

	Basic needs	Specific need Need key indicator	Measure
		Breathing	Pollution rate	Countermeasures for zero-impact
	Physiological needs	Food Shelter	Malnutrition, obesity Homeless rate, working years for buying a house	Catering with healthy food, sur-plus for non-profit organisations Construction of flats for employ-ees & relatives
			Sedentary level	Hospital
			Cardiovascular diseases	Health insurance
		Health	Death rate Absenteeism rate	Gym Rehabilitation centre
			Accident rate	Catering with healthy food
			Average time off work	Ergonomics
	Safety and			Leasing options
	security needs	Employment	Permanent contract Motivation rate	Financing options Training
				Development options
			Two working parents,
		Family	single parent, grandpar-ents in the city, distance	Home-office, kindergarten, per-missions paid
			to work location

Table 4 .

 4 Simulation results for the three levels: company, supply chain and society measures

	Simulation level Key Indicator	No collaborative work	Collaborative work
			Simulation Model	Simulation Model
		Margin per unit	3,680 € / unit	6,750 € / unit
	Company level	Production capacity	740 units / day (t=1,100)	910 units / day (t=1,100)
		Customer order lead time	80 days (t=50)	60 days (t=50)
		Margin per unit	6,000 € / unit	6,750 € / unit
	Supply chain	Customer demand	910 units / day	935 units / day
	level	Customer order lead time	100 days (t=50)	60 days (t=50)
	Society measures of the company	Margin per unit Absenteeism rate Productivity rate	1.600 € / unit 9% 90%	6.750 € / unit 3% 99%


Bjørnar Henriksen

Andreas Landmark

Carl Christian Røstad

What product developers really need to know -Capturing the Major Design Elements

Keywords: Product Development, Fact-based design, Industry 4.0

Digitalization is no longer about finding data or inputs to different business processes as advances in technology now enables us to capture and/or retrieve all needed data. The challenges lie in the quality of the data, not only in the narrowest technical sense of the term, but also in relation to the extent of what e.g. product developers really need to know in order to support their processes and how this should be presented. In four industrial R&D projects, financed by the Norwegian Research Council and the EU, these challenges have been addressed. In the first project, a traditional approach was chosen. Here overall objectives and use-cases were defined, and the project quickly jumped to what everybody (including industrial users and research scientists) believed what the users needed and what could be provided to users from pre-defined data sources. However, it became clear that this did not necessarily match what the users really needed. This is in line with experiences from more traditional process mapping. Through the four R&D projects, a methodology focusing on what the product developer really needs, named "Major Design Elements" was developed, tested and implemented. The approach is to identify the most important design elements, then find what kind of knowledge is required, including relevant analyzes, data and sources. This together with an understanding of the relevant processes form the basis for a Design Dashboard enhancing the product development process.

Introduction

Challenges in fact-based product development

The product development process has become an even more important competitive element with the greater pressure to meet customer needs faster, with more precision and increased customization to individual needs. Digitalization has accelerated and is an enabler for companies to keep up. However, it is not obvious how companies can achieve the desired effects from this "window of technology". The Industry 4.0 paradigm, with its high emphasis on data from the manufacturing process itself, has put more and more focus on process and organization. However, manufacturing companies often are not in the front-seat in developing more fact-based product development processes. This is not necessarily due to lack of technology, but more a question of knowing how to identify a company's need for data, analytics and knowledge -and how to put their existing data to good use, i.e. enabling use.

Through a portfolio of R&D-projects including industrial-and R&D-partners we have tested, developed and described an approach for more fact-based product development. In these projects we have experienced the pitfalls of overly focusing on technology for data capture and analytics (i.e. capturing "facts") at the expense of the core tasks in product development and innovation. In this paper we present an approach developed through these projects. This methodology aims to analyze the need for facts in core activities in product development in manufacturing companies.

The R&D projects and scientific approach

This paper is based on the research carried out in four R&D projects in medium sized manufacturing companies. A denominator for the projects is the objective of improving product development based on facts. The projects consist of many of the same partners, hence the R&D-work has been fertilized between the projects. One is funded by H2020, while three are co-funded by the Norwegian Research Council (NRC). The action-oriented approach means that the researchers have actively worked out solutions the companies can use and implement -in line with traditional action research methodology.

LINCOLN (H2020): 2016-2019, 4 industrial-and 12 R&D-partners. The main objectives are to: (i) develop three types of radically new vessel concepts through simulation model testing, (ii) demonstrate Lean Product/Service Development to the vessel design, (iii) improve ICT tools to support vessels design and operations.

RIT (NRC): 2018-2022, 3 industrial-and 3 R&D-partners. The main objective is to develop a Design Dashboard where large data volumes are analyzed/presented together with other types of data according to product requirements in the leisure boat industry.

RADDIS (NRC): 2018-2022, 4 industrial-and 2 R&D-partners. The main objective is the reduction of physical work using enabling technologies within visualization, product digital twins and simulation. The project also aims to find more proactive ways to deal with regulations within the marine industry.

WRAPID (NRC): 2018-2022, 2 industrial-and 2 R&D-partners. The main objective is to develop solutions for fact-based modularized product design for heavy machinery for agricultural and industrial applications.

2

Perspectives on fact-based design

Knowledge and "facts"

Competence, knowledge and facts are terms frequently used when describing high performance organizations. Gunasekaran and Ngai characterize these organizations by: (i) core competence, networks and cooperation; (ii) process orientation; (iii) free margins;

(iv) learning organizational structures; and (v) knowledge management and information technology [START_REF] Gunasekaran | Knowledge management in 21st century manufacturing[END_REF]. Product development is a source of new knowledge, but we also need to reuse existing knowledge for the generation of new ones. This has to be integrated back into the development cycle to match ever increasingly complexity of design. Knowledge and facts are often used interchangeably. A scientific fact is an objective and verifiable observation, in contrast to a hypothesis or theory, which is intended to explain or interpret facts [START_REF] Gower | Scientific Method: A Historical and Philosophical Introduction[END_REF]. In this paper, we see fact quite similar to what Nonaka and Takeuchi terms explicit knowledge, in contrast to tacit knowledge [START_REF] Nonaka | The knowledge-creating company[END_REF].

Applying the notion and theory of knowledge management to engineering activities; Knowledge-based engineering (KBE) is the application of knowledge-based systems technology to the domain of manufacturing design and production. The design process is inherently a knowledge-intensive activity, so a great deal of the emphasis for KBE is on the use of knowledge-based technology originally to support computer-aided design (CAD). KBE can have a wide scope that covers the full range of activities related to Product Lifecycle Management and Multidisciplinary design optimization. The scope of KBE includes design, analysis, manufacturing, and support [START_REF] Prasad | What Distinguishes KBE from Automation. coe.org[END_REF].

2.2

The product development process

The product design process is extremely important for the competitiveness of manufacturing companies. It may also one of the most complex procesess in a company due to the integration of heterogenous, existing, and new knowledge that must transform requirements and constraints into technical solutions. The choices and decisions taken during the design phases impact other steps and processes in product development, production, logistics etc. [START_REF] Bricogne | Knowledge Based Product and Process Engineering Enabling Design and Manufacturing Integration[END_REF]. Within Lean product development (LPD), knowledge plays an increasingly important role. Design cycles are examples of activities that may provide value or waste, depending on how to look at it. E.g. design process iterations could create valuable knowledge i.e. for variability. However, the specific iteration that can be eliminated without loss of useful knowledge is waste since only information that contributes to reduce risk provides value [START_REF] Reinertsen | Lean thinking isn't so simple[END_REF].

According to Rossi, Morgan, and Shook, Lean product development is about creating value through a process that builds on knowledge and learning enabled by an integrated system of people, processes, and technology [START_REF] Rossi | Lean Product Development[END_REF]. The Lean pioneer Allan C. Ward's core thesis is that the very aim of the product development process is to create profitable operational value streams and that the key to doing so predictably, efficiently, and effectively is to create useable knowledge [START_REF] Ward | Lean Product and Process Development, 2nd Edition[END_REF]. Kennedy prefers to use designations as 'Knowledge-Based Product Development' and 'Learning-First Product Development' instead of LPD. He suggests that PD must be viewed as a 'world of knowledge', rather than a 'world of tasks' [START_REF] Kennedy | Learning First Product Development: Understanding Implementation Principles[END_REF]. According to Kennedy the value stream for knowledge represents the flow of useful knowledge across different projects, products and functional areas -which is a characteristic that most enterprises fail to leverage. This can be seen as "Minimising the waste of knowledge", not originally one of the seven "wastes" in lean manufacturing, but highly relevant in PD.

There is a range of more recent methodologies for product development with close links to LPD. Jill Jusko presents some of these concepts gaining greater attention in the manufacturing product development community [9]:

• Agile Development relies on the ability of small teams and teamwork to make changes quickly and promote intimate customer collaboration. • Interoperability: cyber-physical systems allow humans and smart factories to connect and communicate with each other. • Virtualization: a virtual twin of the Smart Factory is created by linking sensor data with virtual plant models and simulation models. • Decentralization: the ability of cyber-physical systems to make decisions of their own and to produce locally thanks to technologies such as 3d printing. • Real-Time Capability to collect and analyze data.

• Increasing Service Orientation.

• Modularity: flexible adaptation of smart factories to changing requirements Industry 4.0 enables a more effective infrastructure in which the design and development activities of a product's life cycle are closely integrated through real-time information and big data. The challenge is to understand how to use this extensive information in order to enhance product value and to improve industrial productivity. Since information must be displayable, reusable and available in real-time, the fourth industrial revolution is already well-aligned with lean thinking [START_REF] Cattaneo | Product Lifecycle Management and the Industry of the Future[END_REF].

Knowledge requirements -product and process

Even though software development plays an increasing part, product development in manufacturing is normally conducted differently from pure ICT projects. However, as described in the sections above, knowledge and ICT support are becoming more and more important to the product design process. The challenge is then to define what we need, and how to get the knowledge. Generally, the knowledge requirements could be categorized as product-or process knowledge [START_REF] Zha | Knowledge-intensive collaborative design modelling and support -Part 1: Review, distributed models and framework[END_REF]. However, in product design, we will normally need both kinds of knowledge. The common approach for structuring product knowledge is through Product Models, where one of the most common is the "Functional-Behavior-Structure" [START_REF] Gero | The situated function-behavior-structure framework[END_REF]. Depending on the product complexity, the knowledge could be distinguished into [START_REF] Bricogne | Knowledge Based Product and Process Engineering Enabling Design and Manufacturing Integration[END_REF]: (i) Function, (ii) Structure -technical architecture, BOM, (iii) Behavior, (iv) Properties -set of characteristics i.e. material, geometry, technology, etc, (v) Lifecycle. A range of sources and ICT tools such as eBOM, CAD and ERP are used to capture, transfer, analyze and present product knowledge.

The process knowledge is created during the whole lifecycle of a product and is normally based on activity models creating links between products, resources and their characteristics. An activity aggregates several kinds of knowledge such as sequences, functions, rules, states etc. Various types of process mappings, formalized through software tools such as PLM, MPM [START_REF] Hugo | Van Mal: The Structuring of Process Knowledge: Function[END_REF] are common enablers.

3

The R&D projects' focus on product design

The companies

The companies from the marine and agricultural industries participating in the R&Dprojects presented are medium-sized operating in international markets, facing expectations for continuous product improvements and product launches. They have limited staff-resources dedicated to product development and need to combine development with operational activities. However, the companies share an eagerness bringing new technology into their products and processes.

The design processes

Process analysis is a very common approach for understanding the business and context where new ICT solutions are meant to be implemented and give an improvement in performance, quality etc. Figure 1 illustrates a typical product development model.

Fig 1 Product development as an iterative process

The details presented are not important here, but what we see is that the product development in our case companies (Figure 2) are typically very unstructured, with many iterations and diffuse requirements. It is also difficult to identify what facts are integrated in the process and what is really needed for a good product development process. The above process analysis gave an overall picture of the product development context and challenges for the companies but did not provide sufficient insight on how to improve the situation. Hence this traditional process analysis approach didn't identify what kind of analyses, data, and ICT solutions are needed to really improve the process.

Identifying the major design elements

Our need for better ways of defining requirements for facts into the product development made us emphasize the product as such, i.e. product data and the core tasks in the design process.

The challenge is to identify what kind of analyses and subsequentially data we really need in our (technical) design. What are the major design elements of the product? This approach tunes down "what data is available" and/or "what would be nice to have". For some industries and product/markets, the major design elements (MDE) are well known even though the weight of importance could vary. The leisure boat manufacturing industry is such an example, where the product designers are working on well-defined design elements (Table 1). In this case, the next step is to define what analytics and data we should bring to the design dashboard to improve product design processes.

For other industries and product/markets the MDEs are to some extent inherent, but not necessarily well defined. This was the case for the agricultural machine industry. In our projects a methodology that could identify the MDEs and what facts needed to improve the work on these elements has been developed. This was done through workshops where the people involved in the companies' product development participated together with external industrial experts (facilitators). The company was well prepared before the workshop, so much of the workshop was about classifying and grouping the design elements and discussing examples. The above table illustrates the MDEs for our cases in different industries. We see that the design elements the product designers are focusing on in the technical product design have similarities. Hence, some of the data might be captured and analyzed using similar hard-and software solutions. However, it is important to respect the specifics of the industries and companies in defining the MDEs and their need for facts.

When the MDEs are grouped and labeled, they have to be described and detailed to a level where we can see what kind of analyses and the data providing the desired facts to the designers. To some extent, this is about defining a product design dashboard that provides facts and also is a gateway for more advanced analyses and data-drilling. Some of the facts required in a design dashboard for major design elements are already stored in the company systems, e.g. ERP-, quality-and/or product-embedded systems. The challenge is to bring data of a correct quality to the dashboard and create/make available relevant analytics for designers. This phase is the more technical part of the projects where solutions are developed. However, this is also where the cost-/benefit considerations of such a solution comes into force.

A hint of what data we might have and what we could learn

The focus on MDEs assumes that the customer requirements and the overall process is fact-based and would benefit from lean product design approaches presented in 2.2. A basis for the MDE methodology is to focus on the important tasks and challenges in product design, and not focus on ICT solutions. However, in the latter stages, we see that the discussions are more concrete and fruitful when we could give practical examples of data and analytics. In the agriculture case, data from product embedded sensors were used to give examples related to patterns of use (6, 7 and 8 in Figure 3), hydraulics/chamber pressure, etc. (3 and 5 in Figure 3). For the marine industrial cases, several analytical approaches and visualizations are now being tested and implemented based on real-life captured data from prototype vessels and fleet of vessels available. E.g. hull pressure, which can give important data on MDEs such as structures and powering.

A common experience across these companies is the iterative nature of explorative data analysis and PD requirements-driven data analysis. To develop the product developer's intuition about the value of the data in the organization, it seems necessary to couple "product development"-minded data scientists with "data-minded" product developers. Aided by a methodology it is possible to focus the discussions to avoid becoming overly technical-or overly PD-driven.

Conclusion

Today the challenge for manufacturing is more and more about finding smart ways to use data and knowledge that is available through different systems and devices. In product development, we often see different kinds of spiral approaches, where the real need for facts is often difficult to define. Through different industrial R&D projects, a methodology has been developed and tested, that aid companies to capture and present data and analytics for the Major Design Elements in product development. This approach could reduce the time used in product development and improve quality in product and services. In this way the focus on Major Design Elements could enable companies to lean and Knowledge Based Product Development.

•

  Knowledge-based Development is characterized by the creation of reusable knowledge through learning cycles. It incorporates set-based design. • Spiral Development is associated with software development and characterized by multiple iterations of the entire process, unlike staged processes. The Industry 4.0 concept represents a new paradigm as it takes account of the increased computerization of manufacturing where physical objects are seamlessly integrated into the information network. The main features of Industry 4.0 are [11]:

Fig 2

 2 Fig 2 Product development, not a straightforward process in the case companies

Table 1 .

 1 Major Design Elements (MDE) in the marine-and agricultural cases

	Boatbuilders	Type of knowledge	Agriculture equipment	Type of knowledge
		product process		product process
	Hull definition	x		Material properties	x
	Hydrostatics		x	Flow in machines		x
	Weights	x		Weight and geometry	x	x
	Powering		x	Hydraulics capacity	x	x
	Stability	x	x	Material input in machine		x
	Structure	x	x	Robustness	x	x
	Arrangements	x		HSE (Health Security Env.)		x


Lisa Hedvall
email: lisa.hedvall@ju.se

Joakim Wikner

Decision-making process for buffer dimensioning in manufacturing

Keywords: buffer dimensioning, dimensioning process, service performance management

Systematic and stochastic variations, both endogenous and exogenous to companies, are a constant challenge for decision makers struggling to maintain a competitive advantage for the business. In response the decision maker introduces buffers to absorb variations but this does not target the source of the problem. The first step should instead be to focus on how to reduce variations and then to handle the remnant variations. In summary the first step should be to perform variation management and then as the second step buffer management should be applied. The combination of these two subprocesses represent service performance management and within this context is buffer dimensioning a key challenge. Input data, decision maker and process logic are identified as three key aspects of buffer dimensioning which are integrated and resulting in six scenarios. These scenarios unravel different conditions for performing buffer dimensioning and facilitate an awareness of a match or mismatch between current and desired situation.

Introduction

Competitiveness can be based on different business characteristics such as patents protecting a market, a unique brand that attracts customers or a value-adding flow aligned with customer requirements. From a manufacturing perspective the flowbased type of competitiveness is of particular interest since this is where manufacturing is active in providing the foundation of competitiveness. A swift and even flow is in line with the one-piece continuous flow portrayed as the ideal state, according to lean thinking. This state is however seldom reached except for in isolated segments of the flow. Instead, a holistic picture of the flow unravels a number of discontinuities that represent variations in different shapes. Such variations may occur along the flow in terms of e.g. different lot sizes or capacity availability but can also be exogenous and related to characteristics of demand or external supply. Even though there are a wide set of variations, both systematic and stochastic, they have in common that they represent major challenges to decision making. Frequently the countermeasure to variations is to introduce buffers. This approach does however overlook the potential in first addressing and reducing the variations, and as a consequence also reduces the need for buffers. The challenge is that there are numerous sources of variations affecting a business. In other words, it is not enough to only consider one type of variation, it is important to manage all variations that have significant impact on the delivery capability, and hence the competitiveness. Previous studies have even showed that variance reducing activities have a greater impact on the output rate than a buffer such as safety stock [START_REF] Hurley | Inventory and capacity trade-offs in a manufacturing cell[END_REF]. However, it is most often neither economically sustainable nor viable trying to eliminate all variations. Variation management (VM) with the intention to reduce variations is therefore recommended as a first step before addressing variations by the use of buffers. The total amount of variations sets the preconditions for the need of buffers, realized in terms of safety stocks, safety capacity or safety lead-time, that can be used to absorb variations and protect the delivery capability.

To actually manage buffers is referred to as Buffer management (BM), which complements VM, and concerns decisions regarding the selection, positioning and dimensioning of buffers. In the current body of knowledge, studies have showed that an additional amount of resources can, for example, reduce lead-times [START_REF] Fry | A service-oriented manufacturing strategy[END_REF] and increase responsiveness [START_REF] Van Mieghem | Commissioned paper: Capacity Management, Investment, and Hedging: Review and Recent Developments[END_REF]. BM should therefore not be confused with reducing variations through VM. BM is rather a response to this and an approach to absorb variations [START_REF] Shoaib-Ul-Hasan | A routinebased framework implementing workload control to address recurring disturbances[END_REF]. This is an important notion since buffers will not reduce the causes that create challenges through variations, rather it is a symptomatic treatment to decrease the effects of variations. In order to minimize the effects of variations, by utilizing buffers, it is of outmost importance that the right buffer and buffer size is configured. Given that decisions are made for the type of buffer and position, the decision of buffer size remains. Buffer dimensioning refers to the process of the latter concern, focusing on determining the right buffer size based on the requirements by utilizing different dimensioning methods. For materials the literature is vast with for example different methods to calculate an appropriate safety stock level for certain circumstances and trade-offs between different cost components. However, for safety capacity and safety lead-time the theoretical support is not as prominent. This is also observed in industrial practice where managers have expressed a lack of decision support for dimensioning in general, independent of the type of buffer [START_REF] Hedvall | Buffers in Capacity Management: A multiple case study[END_REF]. The purpose of this research is therefore to outline the key aspects of buffer management, with focus on the implications for the decision-making process for buffer dimensioning.

This research builds on insights from practitioners and is a conceptualization of findings from a research project. The remainder of this paper is structured as follows. First, a theoretical frame of reference is presented where VM and BM are briefly described. Thereafter an integrated process of VM and BM is discussed from a general perspective for the key aspects included in service performance management, followed by a detailed perspective for how buffer dimensioning varies depending on the context. Finally, the insights from this research are interpreted in terms of managerial implications by some concluding remarks.

Theoretical frame of reference

The process for establishing service performance is in focus here and it encompasses variations management and buffer management. This chapter is therefore dedicated to the parts that influence the service performance.

Variation management

All companies are to some extent exposed to variations. Endogenous variations in processes have been the focus of attention in quality management literature due to the fact that variations exist in all sort of processes and that variations are sources to quality problems [START_REF] Yu | A strategy perspective on total quality management[END_REF]. Exogenous variations as demand is, however, often mentioned as the most challenging type of variations and that often have a direct impact on performance [START_REF] Caridi | Improving materials management effectiveness: A step towards agile enterprise[END_REF]. It is important to map existing variations, monitor the effects and reduce the variations to be competitive [START_REF] Nikulin | Resolution of reliability problems based on failure mode analysis: an integrated proposal applied to a mining case study AU -Viveros[END_REF]. This management of variations is here referred to as variation management (VM) with focus on variation reducing activities.

To identify the source of uncertainty is a first step to reduce variations but it does not automatically reveal the cause that drives the variations. In order to fully map variations it is important to identify the source and cause to find appropriate actions that reduce the variations. How well a process is studied and the information available determines the possibility to identify variations and thereby also the possibilities to reduce the variations. One important notion is that the variations, independent of source, can have different characteristics depending on the time horizon concerned. In general, the variations a company is exposed to can be classified as systematic or stochastic variations that in general represent variations of long-term patterns or shortterm random patterns.

Buffer management

Variations that remain after the application of VM can be handled by buffers to absorb variations and thereby reduce negative effects on business performance. Managers need to select appropriate buffers, positioned where needed and dimensioned to the right size to sufficiently achieve this purpose. This work is referred to as buffer management (BM). Buffer selection is mainly about choosing what type of buffer to use and for what purpose. The first feature is if the variation(s) the buffer is supposed to absorb is of systematic or stochastic character. Next the buffer selection is decided based on what type of buffer is appropriate for the specific variation. As mentioned in the introduction, buffers can be in terms of additional material, additional capacity and additional lead-time [START_REF] Caridi | Improving materials management effectiveness: A step towards agile enterprise[END_REF]. Within the technique drum-buffer-rope a set of rules for buffer positioning is proposed with buffers referred to as constraint, assembly and shipping buffers depending on their position. When the buffer selection and positioning is determined, the focus shifts to the dimensioning of the buffer size, referred to as buffer dimensioning.

Buffer dimensioning

A buffer should hedge variations, for example unexpectedly high demand. If variations are not reduced or absorbed, they can create varying lead-times and negative effects on the delivery performance [START_REF] Boute | Coordinating lead times and safety stocks under autocorrelated demand[END_REF]. Established buffers can be used to absorb deviations from the normal requirements of materials, capacity or lead-time. The size a buffer should be depends on the amount of existing variations weighted to the costs implied to hold the buffer, in order to achieve a balance between costs, the safety provided and the risks the company is prepared to take. How extensive the safety should be is in turn determined by established performance measures. These buffer decisions are in general covered in the buffer dimensioning where the focus is on determining buffer size.

Service performance management and buffers

Competitiveness rests on the capability of rendering service to the customers. A major challenge for competitive service performance is to reduce variations and to absorb variations through appropriate buffers. VM and BM can therefore be regarded as fundamental parts for service performance management, with the aim to meet customer needs by high service performance. In Fig. 1, it is illustrated that the subprocesses of the service performance management is influenced by three main aspects: input data, decision maker and process logic. Buffer dimensioning, as a part of BM, is the focus of attention in this research and how the main aspects can influence the decisionmaking process of buffer dimensioning is emphasized below.

Fig. 1. Service Performance management process

Input data

Buffer dimensioning varies depending on the amount and type of available data, methods and system support. Valuable input data are for example the remaining variations after application of VM. In this research the input data is differentiated in term of quantitative or qualitative data. Input data that are measured and included in the dimensioning process are regarded as quantitative while parts that are hard to measure or simply not measured, but still considered in the dimensioning process, are referred to as qualitative input data. The amount and type of input data may be used in dimensioning but can also affect the methods used, and two main categories of methods can be identified. The first category is here referred to as assessment methods that relates to more or less intuitive and experience-based ways to determine buffer size without formally performed calculations. It can be regarded as informal methods for decision making, e.g. deciding that a buffer should be of certain size based on what has been required previously (by experience), what feels appropriate (intuition or gut feeling) or as a proportion of demand or other appropriate measures. The last example implies that simple calculations are done but as the proportion variable is to a large extent estimated this method is considered as an assessment method. The second category is referred to as computational methods and regards more formal ways to calculate the buffer size by considering statistical distributions of prevailing variations. It can for example be to calculate the buffer size based on desired service level or as a trade-off between carrying costs and shortage costs. These types of methods have been extensively researched for material buffers but are not as established for capacity and leadtime buffers.

Decision maker

Two categories of buffer dimensioning methods were identified above, one referred to as assessment methods, relying mainly on experience, and the other referred to as computational methods, based on more formal calculations. For a decision maker the decisions can also be either more based on experience or relying on the method applied. The latter is here referred to as rational decision maker, implying that decisions are systematically decided based on facts. A rational decision maker often employs a series of analytical steps to review input data and possible outcomes before proceeding with a decision. If subjective judgement and assessment are involved it is difficult to apply fully rational reasoning. For this matter, when a decision maker instead relies on experience in the decisions it is here referred to as an intuitive decision maker meaning that information processing can be regarded as non-sequential and based on previous experiences, emotions or implicit knowledge. This infers that the decision making is subject to influence by the decision makers experiences beyond common sense, sometimes even based on an intrinsic feeling that can be referred to as gut feeling, instinct or inner sense. This type of decision maker can thereby receive input or ideas to subjectively use in the decision making without always knowing exactly where the input came from.

Process logic

The discussion has so far focused on different aspects affecting the decisions and not the process of buffer dimensioning as such. Decision making regarding buffer dimensioning can be based on expected future requirements and feedback of the actual state of buffers. Two main process logics can therefore be identified: conventional and adaptive process logic. The former represents processes where control signals are processed based on controlled variables and actions made when variables are outside control limits. An example could be to replenish a material buffer that is below its target level. An adaptive process logic on the other hand embrace an iterative approach and adjust controllers to achieve or maintain a desired level of performance when variables are unknown, uncertain and/or change. This means that the buffer dimensioning is adapted based on changing conditions, either by updating parameters or changing the dimensioning method depending on prevailing circumstances.

Scenarios for buffer dimensioning

The main aspects identified above for buffer dimensioning are input data, decision maker and process logic. In combination they influence and create different scenarios in the decision-making process for buffer dimensioning. An integrated perspective of the main aspects is presented, followed by a discussion of scenarios resulting from this integration.

Integrating the buffer dimensioning aspects

The three aspects influencing buffer dimensioning are outlined above. Even though each aspect is interesting on its own it is in the integration of the three that defines the buffer dimensioning process and its main characteristics. The quality and type of input data and the type of buffer dimensioning method can influence how the method is utilized and how the dimensioning process is performed. A typical example for a fully rational decision-making process would be that a computational dimensioning method is used and that the decision maker applies the calculated buffer size to the manufacturing system without adaptions. The other extreme would be that an assessment method that is only based on gut feeling is utilized, representing a genuinely intuitive situation. Although, there might also be situations where the decision maker has previous experiences that a certain computational method is not providing sufficient performance and that manual modifications are applied to the calculated value. The buffer dimensioning would then be partly rational and partly intuitive. However, if required input data is not available, measured or observed for a computational method, or there is a lack of computational methods available for the buffer of interest, it is hardly possible to be fully rational. Finally the decision maker may employ adaptive control in utilizing the input or adapting the dimensioning method employed based on obtained service performance. However, if no explicit adaptation is employed the approach is referred to as being conventional. This discussion has highlighted that input data can be quantitative or qualitative, with a decision maker that can be rational or intuitive, and with a process logic that is adaptive or conventional. In total, the combination of these aspects provides three dimensions and creates eight different scenarios in the buffer dimensioning which is illustrated in Fig. 2 as a cube.

Fig. 2. An integrated perspective of buffer dimensioning aspects

The cuboids should not be perceived as mutually excluding but rather as representing a continuum with different characteristics. In most cases there are no clear dividing lines between fully rational or fully intuitive, but rather that one aspect is more prevalent than the other and in total there are eight main categories. The buffer dimensioning within a company could therefore be classified within the cube based on the characteristics, perhaps with varying positions in the cube for different buffers. The current situation for buffer dimensioning in relation the individual decision makers preferences or in relation to desired situation can create a match or mismatch.

Six scenarios for buffer dimensioning

The cube in Fig. 2 highlights eight different cuboids that represent a unique combination of different scenarios for buffer dimensioning. However, the rational decision maker cannot rely on genuinely qualitative data and hence there are only six cuboids that represent viable buffer dimensioning strategies. Several of the identified relationships have been indicated in a research project where the buffer dimensioning process to a great extent is based on assessment methods due to a lack of decision support, especially for capacity buffers where there is a lack of established buffer dimensioning methods. For material buffers, which are well covered in the literature, the situation is slightly different with higher degrees of calculations and less of intuitive influence. One company have a fully rational approach for dimensioning of safety stock. For this company the buffer is dimensioned based on an advanced formula that only a few employees are fully knowledgeable of, which could explain why manual and more intuitive changes are not commonly employed.

Conclusions

A general process for service performance management has been established to be applied and provide support in the management of different types of variations in combination with buffers in terms of additional materials, capacity and lead-time to absorb the variations. The main aspects (input data, decision maker and process logic) contribute to more awareness of what constitute and affect the buffer dimensioning process, thereby creating better conditions for making appropriate changes for a mismatch between current and desired situation. Further studies can refine preconditions, challenges and opportunities for the different buffer dimensioning scenarios, outline what is needed to change to another dimensioning scenario, investigate where different types of buffers tend to be in terms of dimensioning scenarios and thereby highlight where more research is needed from a theoretical and practical perspective.

Acknowledgement

The study has been performed within the project KOPability which is funded by the research environment SPARK at Jönköping University (through the Knowledge Foundation) and the six participating companies.