Woonsang Baek

Duck Young Kim
email: dykim@unist.ac.kr

In-process noise detection system for product inspection by using acoustic data

Keywords: In-process inspection, Vibration sensors, Acoustic sensors

Objective quality inspection of products in manufacturing process is inseparable from sensor technologies. Inspection methods using analysis of vibration signals have advantages such as being non-destructive, accurate, fast for in-process application. This paper presents recent developments and applications of in-process product inspection which use vibration and acoustic data in various industry. In detail, the inspection system developed with accelerometer, laser vibrometer, laser ultrasonic sensor, acoustic emission sensor, and microphone are presented. An in-process noise detection system for car body parts inspection is introduced as a case study.

Introduction

Increasing awareness about quality of products have improved performance of in-process inspection in highly automated manufacturing process. Since the manufacturing process vary from molding, machining, and so on, numerous quality indicators, which are measurable physical manifestations of the products, have been suggested. Inspection with as many indicators as possible should provide plenty information of the products, however, limitations of takt time and cost hinder the usage of multi-indicators. Therefore, selecting proper quality indicators according to characteristics of the inspection process has been a critical issue. For instance, visual inspection with computer vision technology is one of the representative inspection method that measures dimensional error or color of the products. Not only for the manufactured products, but also it has been applied to inspection of raw products such as agricultural foods [START_REF] Narendra | Quality inspection and grading of agricultural and food products by computer vision-A review[END_REF]. However, the visual inspection is not the best solution when it is necessary to inspect inside of the product. In this cases, inspection using vibration data can be an alternative solution since it has strength in detecting changes and dynamics inside of the products [START_REF] Chui | Evaluation of wood poles using a free vibration technique[END_REF]. However, in the past decades, most of the inspection using vibration data have been applied to condition monitoring of industrial machineries, not for inspection of the products. The reason is that the vibration sensors should had to be contacted with the surface of the target. Moreover, developing apparatus to fix the vibration sensors at the surface is improper for industrial applications [START_REF] Rodriguez | Comparative study between laser vibrometer and accelerometer measurements for mechanical fault detection of electric motors[END_REF].

Recently, advances in sensor technologies enabled various types of vibration sensors which are capable of remote measurements of vibration signals. Also, improved signal processing algorithms have increased precision of the inspection. Therefore, numerous researches adopted the vibration sensors for product inspection.

Our paper reviews the state-of-the-art technologies for in-process product inspection methods using the vibration sensors. In detail, inspection systems which installed accelerometer, laser vibrometer, laser ultrasonic sensor, acoustic emission sensor, and microphone are presented.

Moreover, the solution of the practical problem, that developing an in-process inspection system for a car body part is introduced, based on the state-of-the-art technologies. The presence of the irritating small noises that caused by assembly defects of the car body parts are determined by a noise detection algorithm.

Vibrations sensors for inspection

Product inspection methods using the vibration and acoustic data, assume that assembly errors, random discrepancies of components have potential to be manifested in abnormal vibratory behaviors. Traditionally, the vibration data have been measured by contact-type sensors, however, the contact type sensors require sensing part that is in contact with the products. Non-contact type sensors recently have aroused as an attractive Fig. 1. Illustration of five inspection methods using vibration sensors alternative due to its simple implementation. Post-processing analyses of vibration data make use of the features in time domain, frequency domain, and cepstral domain [START_REF] Aryan | An overview of non-destructive testing methods for integrated circuit packaging inspection[END_REF].

Considering variety of the sensors, five major categories are created to classify the existing inspection methods. Inspection methods using each type of sensors are as follows: inspection using accelerometer; inspection using laser vibrometer; inspection using laser ultrasonics; inspection using acoustic emission sensor; inspection using microphone. Here, the selected representative papers are introduced as below.

Inspection using accelerometer

Accelerometer is a seismic type sensor that consists of a spring-damper system, and a piezoelectric crystal, to create an electric signal proportional to the acceleration of the measured vibration. The sensor is mostly installed at the surface of the product that being measured. Most of the previous studies have monitored the continuous vibration response of fixed machinery for condition monitoring. However, there are several applications that adopted the sensor for inspection of products.

The inspection system for microchip packages, which measures quality of sealing, had installed the accelerometer at the vibrating fixture. Signal processing techniques, such as PCA (Principal Component Analysis) and statistical process control were applied to determine the status of collected data [START_REF] Ostyn | Statistical monitoring of a sealing process by means of multivariate accelerometer data[END_REF]. In case of inspecting injection-molding process, checking existence of the flash that caused by imperfect plastic resin is the main concern. Accelerometers were installed at the fixture of the inspection system to collect the dynamic trend of the mold movement during the filling stage. Data captured by Z direction of the accelerometer was in particular analyzed to extract feature signal, and logistics regression model was applied for determination of the state [START_REF] Zhang | Development of an in-process pokayoke system utilizing accelerometer and logistic regression modeling for monitoring injection molding flash[END_REF].

Assembly process of automotive assembly with robot arm and gripper, had applied the accelerometer to monitor the quality of assembled product. The traditional assembly monitoring system consists of force and moment sensors, was supported by analysis of the vibration data. Various features in frequency domain were extracted, then threelayered neural network were applied for automatic classification of the defects [START_REF] Mechefske | Failure detection in automotive light assemblies during vibration endurance testing[END_REF].

Inspection using laser vibrometer

Laser vibrometer measures the vibration velocity of objects using Doppler effect while providing high spatial resolution in a wide frequency band. In addition, the laser vibrometer has its strength in easy application, which reduces the cost for developing the inspection station. In several industrial cases, the vibrometer has proven their advantages over the accelerometers, especially when the contact measurement is not available, or contaminate the target signals.

A comparative study of electric motor inspection measured the vibration data by using the accelerometer and the laser vibrometer. The study shown that the data measured by the laser vibrometer derived higher correlation with defects than that of the accelerometer. The research mentioned that the data of accelerometer was contaminated by the electromagnetic noise from the surface of the motor that distorts frequency spectra and compromise classification through frequency analysis [START_REF] Cristalli | Mechanical fault detection of electric motors by laser vibrometer and accelerometer measurements[END_REF]. For extracting the quality indicator of fruits, especially for firmness, which is not easily measured by a visual observation, the power spectrum analysis was applied to detect differences in displacements inferior to the nanometer [START_REF] Santulli | Development of a method for nondestructive testing of fruits using scanning laser vibrometry[END_REF].

On-line inspection system of washing machines installed the laser vibrometer at right above the position that the products be fixed for repeatable inspection procedures. The system concentrated on detecting abnormal vibrations of the tub that usually generated by the faulty operation of motor. Determination of the state was conducted by measuring features in frequency domain after reducing the speckle noise from the collected signal [START_REF] Torcianti | Non-contact measurement for mechanical fault detection in production line[END_REF]. In the other research, the similar inspection system was installed at the mobile robot warehouse inspection, while analyzing the power spectrum of the collected signals for detection of abnormal vibration [START_REF] Raffaeli | Virtual planning for autonomous inspection of electromechanical products[END_REF].

Inspection using laser ultrasonics

Laser ultrasonic sensor is a non-contact type sensor that have been widely used for nondestructive inspection of structures, and products. The sensor collects the vibration response of the surface where a brief laser pulses are induced by pulse generator. Then, a heterodyne, fiber-coupled laser interferometer measures the out-of-plane displacement of the surface. Since the laser ultrasonic sensor has its strength in measuring vibration signals of rough surfaces, it has been applied to various industrial inspections. Inspection of chip-scale packages can be the representative case of the laser ultrasonics application for product inspection. The inspection systems were developed to measure unexpected cracks inside of the multilayer ceramic capacitors. Both features of time-domain and frequency domain were extracted, and compared for fault detection in real-time [START_REF] Erdahl | Online-offline laser ultrasonic quality inspection tool for multilayer ceramic capacitors-Part I[END_REF].

The laser ultrasonic sensor is also assumed to have potential advantages for the inspection of composites. Inspection of the products in aeronautic industry is one of the popular application, since the products are mostly large, complex, and assembled with various materials. An automated inspection system for composite wing plate installed the laser ultrasonic sensor at the mobile robot for full inspection, and developed an algorithm that detects cracks by using Laplacian filter and standing wave extraction [START_REF] Dubois | Evolution of industrial laser-ultrasonic systems for the inspection of composites[END_REF]. In case of automotive industry, laser ultrasonic sensors were applied to inspection of defects that are generated during spot welding, friction stir welding, painting, and checking strength of adhesive bonds [START_REF] Blouin | Applications of laser-ultrasonics to the automotive Industry[END_REF]. Synthetic Aperture Focusing Technique (SAFT) algorithm was developed and applied to the laser ultrasonic data to measure the defects of welded spots [START_REF] Lévesque | Synthetic aperture focusing technique for the ultrasonic evaluation of friction stir welds[END_REF]. Inspection of painting focused on measuring thickness of painted area by obtaining a resonance spectroscopy with the laser ultrasonic sensor. The determination of the status was conducted by comparing the amplitude spectra with the model for propagation in multilayers.

Inspection using acoustic emission sensor

Acoustic emission sensor collects the transient elastic waves which are generated by the rapid release of energy from a localized source or source within a material. The sensors are widely applied to the field of condition monitoring with its advantage of high sensitivity regarding dynamic processes or changes in a material. Although the sensor has limited applicability as the other contact type sensors, there have been researches applied the sensor for the in-process inspection of products.

Recently, additive manufacturing adopted the acoustic emission sensors for measuring the internal defects rather than the traditional monitoring methods, such as temperature monitoring [START_REF] Lu | Additive manufacturing process monitoring and control by nondestructive testing techniques: challenges and in-process monitoring[END_REF]. The acoustic vibration signals were analyzed by extracting various characteristics of frequency domain, and signal processing methods, such as collaborative neural networks [START_REF] Yoon | A PHM approach to additive manufacturing equipment health monitoring, fault diagnosis, and quality control[END_REF]. Similarly, inspection system for stir welding and laser welding installed the sensor at the surface of the galvanized steel for detecting internal dynamics by using signal processing methods [START_REF] Gu | Resonant acoustic emission during laser welding of metals[END_REF].

In addition, inspection system for beverage container that checks the existence of unwanted materials inside of the container installed the sensor. The pulse compression method was applied for data preprocessing, and tomographic reconstruction method was applied for defect detection [START_REF] Ho | Inspection of drinks cans using non-contact electromagnetic acoustic transducers[END_REF].

Inspection using microphone

Similar to the acoustic emission sensor, a microphone was applied to the condition monitoring of machineries during manufacturing process. The methods are useful in certain cases, especially when it is impossible to access the machine since audio measurements can be performed at a distance from the machine. However, the microphone based methods were not developed and widely applied as the other vibration based inspection methods. This is due to the possibility of contamination of the acoustic signal by unexpected noises such as shop-floor noises [START_REF] Vilela | Machine and industrial monitorization system by analysis of acoustic signatures[END_REF]. Therefore, application of the microphones always requires additional apparatus for reducing the shop-floor noise, or signal processing methods for suppressing, or removing the noise.

Inspection system for cracks of rice grain after milling process installed the microphones with an equalizer and PVC pipes for insulating the shop-floor noise. The microphones collect the acoustic signals that generated right after the grains crashed into the plate [START_REF] Buerano | Microphone system optimization for free fall impact acoustic method in detection of rice kernel damage[END_REF].

In automotive industry, traditional inspection of irritating noises in car body parts have been done by human ear. Recently, researches about automatic and objective judgement of that issue have studied with the microphones [START_REF] Bernard | The development of a sound quality-based end-of-line inspection system for powered seat adjusters[END_REF]. The acoustic signals were collected near a fixture, that vibrates the products to resemble the driving situation. The dominant sound quality metrics in a multiple linear regression equation was applied for inspection algorithm [START_REF] Cook | End-of-line inspection for annoying noises in automobiles: Trends and perspectives[END_REF].

3

In-process noise detection system for car body parts

Recently, annoying noises of car body parts have been critical issues for automotive industry, since it can easily degrade the emotional satisfaction of drivers. The noises are caused by a lot of potential sources, for instance, error in assembly operations, defects in components, and dimensional control issues [START_REF] Bernard | The development of a sound quality-based end-of-line inspection system for powered seat adjusters[END_REF]. Acquisition of the annoying noise has been conducted by the microphones [START_REF] Cook | End-of-line inspection for annoying noises in automobiles: Trends and perspectives[END_REF]. However, the shop floor noises are known to easily contaminate the signals of interest as mentioned in section 2.5. Two methods have proposed to reduce the effect of the shop floor noises: conducting the inspection in an anechoic chamber, or applying the noise reduction methods. However, anechoic chamber is not used for full-inspection of real manufacturing process due to its expensiveness costs. The realistic solution for full-inspection are assumed to be the application of noise reduction methods. However, most of the inspection system about the annoying noise have utilized the anechoic chambers, while the noise reduction methods were not seriously studied. Therefore, the author proposed an inspection system for car body parts, as shown in Fig. 1. The system is applicable for manufacturing process by developing noise reduction system with two groups of microphones [START_REF] Baek | An in-process BSR-noise detection system for car door trims[END_REF]. A pneumatic pusher and fixtures were installed for replicating a situation that a driver pushing a car door. A car door trim is slowly pressed down by the pneumatic pusher with a pressure of 10 kgf/cm 2 . The groups of microphones, which are a microphone array and parabolic microphones, collect the acoustic signals during the operation of the pneumatic pusher. The microphones of the microphone array are installed right above the fixture that holds the product, where those of the parabolic microphones are installed at the outside of the system to concentrate on collecting the shop-floor noise. The microphones were grouped to collect real-time data of shop-floor noise, while the most of the existing noise reduction methods use trained shop-floor noise. With assumptions about the sound sources of two groups of microphones, spectral subtraction method and the modified Non-Negativity Matrix Factorization were developed for extracting the annoying noise from the contaminated signal.

Conclusion

The paper reviews the current quality inspection of products during manufacturing process. Since inspection methods using analysis of vibration signals have advantages such as being non-destructive, accurate, fast and applicable for in-process inspection, the focus of review is on the vibrational sensors. Applications of in-process product inspection by using five categories of the sensors are presented, which are inspection using accelerometer, laser vibrometer, laser ultrasonic sensor, acoustic emission sensor, and microphone are presented.

As a case study, the in-process noise detection system for inspection of car body parts is presented. The configuration of developed system, named in-process noise detection system, is briefly introduced. The system was developed for replacing humanbased inspection by devising noise detection algorithms with acoustic data. Since the shop-floor of manufacturing line generates shop-floor noise that may contaminate the acoustic signals, noise reduction methods are also developed. The system installed two groups of the microphones to improve the noise reduction methods.

Fig. 2 .

 2 Fig. 2. In-process noise detection system of the present work of the author


Roman Wdowik
email: rwdowik@prz.edu.pl

Chandima R M Ratnayake
email: chandima.ratnayake@uis.no

R M Chandima Ratnayake

Open Access Digital Tools' Application Potential in Technological Process Planning: SMMEs Perspective

Keywords: Digital Tools, Open Access, Small-Medium Scale Manufacturing Enterprises, Process Planning, Digitalization

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

Introduction

Motivation

Digital transformation, such as: cloud data computing techniques (CDCT), machine learning (ML), virtual reality (VR), cloud-based tools (CBT), augmented reality (AR), artificial intelligence (AI), data-driven production control, etc., offer new opportunities to enhance the operational performance of mechanical and manufacturing engineering [START_REF] Chryssolouris | Digital manufacturing: history, perspectives, and outlook[END_REF][START_REF] Enzo | Data-driven production control for complex and dynamic manufacturing systems[END_REF][START_REF] Tarchinskaya | Cloud-based engineering design and manufacturing: State-of-the-art[END_REF]. In addition, fast-growing digital transformation leads to a demand for more personalized, connected, smart, and sustainable products and services [START_REF] Moghaddam | Reference architectures for smart manufacturing: A critical review[END_REF]. Although the availability of information is immense, due to digitalization, it is very important to share and use it through the effective use of computer networks and at the lowest possible cost (e.g. by the use of open access tools) [START_REF] Calvo | Towards a methodology to build virtual reality manufacturing systems based on free open software technologies[END_REF]. Although the aforementioned is novel, there is significant level of challenges which exist in small-and medium-scale manufacturing enterprises (SMMEs), especially in the implementation into their shop floors of the newest, fast-growing digital technology, often with limited funds and availability of other infrastructure resources such as human resources, R&D and training centers, etc. Technological process planning (TPP) plays a vital role in manufacturing environments, due to the fact that it can be considered one of the most important stages of production, especially from an overall productivity enhancement perspective [START_REF] Wdowik | Application of process parameters in planning and technological documentation: CNC machine tools and CMMs programming perspective[END_REF]. TPP provides a foundation for all manufacturing success, leading to the definition of machining operations' order, choice of necessary fixtures, tools and parameters of processes which are run using CNC machine tools and technological equipment [START_REF] Feld | Basics of technological process planning of machines' parts (in Polish). Wydawnictwa Naukowo-Techniczne[END_REF]. Although the efforts regarding the implementation of CAPP tools [START_REF] Zhang | Computer Aided Process Planning: The state-of-the-art survey[END_REF] and CAD/CAMs [START_REF] Kutin | Integration of different computer-aided systems in product designing and process planning[END_REF] are well known, and these digital tools have proved their usability in digital transformation, contemporary advanced digital and smart manufacturing redefines overall success in relation to the optimization of: design, production, supply chain and customer service perspectives [START_REF] Zhang | Computer Aided Process Planning: The state-of-the-art survey[END_REF]. In this context, it is vital to investigate how to reinvent TPP to cater for the latest and expected future needs and how to enable the expansion of startups and SMMEs.

To reinvent TPP to be efficient and effective requires making use of expert knowledge, together with artificial intelligence (AI), databases, together with machine learning techniques, as well as analysis of market trends and the implementation of various digital tools (DT) such as software and/or devices. In addition, cloud-based techniques could be used [START_REF] Tarchinskaya | Cloud-based engineering design and manufacturing: State-of-the-art[END_REF]. In this context, SMMEs need appropriate strategies and plans, in order to accelerate their development and to be able to compete with potential competitors. Various aspects enable the aforementioned to be achieved. For instance, currently there is a trend to use digital tools with common software platforms (e.g. 3D systems, Autodesk® BIM 360 Design, etc.) to share the data, accelerate TPP, test the prototypes (i.e. in a physical or virtual environment) or operate existing hardware (e.g. CNC machine tools). In this context, SMMEs must make a decision regarding investments and, at the early stage of their operation, whether they need to use open access (free or low-cost) digital tools or to buy other tools existing in the market. Hence, this manuscript mainly presents findings and suggestions, focusing on the use of digital tools in SMMEs, and provides a concise discussion on existing possibilities for the use of digital tools in SMMEs, which use several CNC machine tools in a manufacturing environment. Furthermore, it takes into account the groups of digital tools, their examples, limitations and approaches for their use in TPP. In addition, it suggests the potential to increase R&D work, focusing on free digital tools.

Classification of Digital Tools used in TPP

The digital tools, which are used in TPP, may be classified into groups such as: Software tools (S), e.g. computer applications; Devices (D), e.g. PCs, smartphones, tablets, smartwatches, etc.; machines, machine tools and technological equipment (MTE), e.g. CNC machine tools; technological equipment (e.g. tools, chucks, presetters, etc.); and Internet tools (IT), e.g. webpages, webguides, webcatalogues, internet calculators, internet clouds, internet search engines, etc. The classification proposed by the authors is presented in Fig. 1. It classifies the most important DT on the basis of expert knowledge, experience and discussion with industrial partners. However, the number of DT in SMMEs is limited compared to those in large firms, due to the costs generated by DT.

Perspectives of Technological Process Planning in SMMEs

There are several approaches in TPP which can be considered by industrial companies (refer to Fig. 2). The first one, standard process planning, mainly uses the expert knowledge of a process planner and is a combination of experience, tests and the use of guidelines. It has been used for many decades by process planners. SMMEs which, in general, are careful when they face new investments, have so far used this approach.

In the authors' opinion, it is mainly caused by availability and low expenditure. Recent decades have proved that the application of CAD/CAM systems may help to eliminate paper documentation, accelerate and automate the manufacturing processes of complex parts and provide great opportunity for safer (digital) simulation of manufacturing operations which will be run on real machines.

In parallel with the abovementioned two approaches, it can be beneficial to invest in completely new solutions for TPP, which are based on immersive technology (e.g. VR) and AI. Immersive technology may provide the opportunity to prepare and test technological processes in a virtual environment, while AI may lead to automation. It can be adapted both to VR/AR and standard approaches. Fig. 2 presents the level of complexity of the aforementioned approaches and shows approximate, time-related implementation and possible future scenarios.

In the case of SMMEs, the use of immersive technology may be beneficial because it still provides an opportunity to use expert knowledge. However, all TPP tasks are to be performed in one virtual environment. The limited resources of SMMEs are, in this case, easier to implement into VR than in the case of large enterprises. In addition, in the case of various SMMEs, immersive testing of manufacturing processes seems to be more important than automationmainly if open access virtual realities were to be

Software (S)

PC software software for mobile devices such as smartphones, tablets, laptops

Machines and technological equipment (MTE)

CNC machine tools measuring devices (CMMs, tool presetters) furnaces assembly stations

Technological process planning with the use of digital tools Devices (D)

smartphones and smartwatches tablets laptops virtual reality/augmented reality devices

Internet tools (IT)

internet-based tools such as Web pages, electronic catalogues delivered. AI may play a crucial role in less complex manufacturing tasks and infrastructures (e.g. SMMEs' infrastructures), which do not require advanced expert knowledge and the analysis of many variables and external noise, and also the implementation of AI algorithms should be cost-effective.

The next years will also be important because of the implementation of faster communication techniques. The new trend, which can now be observed regarding the implementation of 5G mobile networks may be very beneficial for SMMEs. This new technology may allow the company to be managed more efficiently. The 5G networks could provide the opportunity, e.g. in several years/decades, to start many SMMEs. The infrastructures of SMMEs will be susceptible to wireless communication. In this context, resources existing in different places will be connected and used for the aims of new SMMEs' development. This will be very important at the start of an SMME. Connecting advanced manufacturing resources is much faster in this case.

Existing Open Access Digital Tools and their Availability for SMMEs

The main issue regarding open access digital tools (OADT) concerns the effective strategies of their use in SMMEs. For PC: CamBam (partially free), FreeMILL, G-Simple

Calculators

Calculations of power requirement in machining, the choice of depths of cut, roughness, dressing conditions for grinding, etc.

Web/Mobile Apps: Walter feeds and speeds, Dr Kaiser App

Internet tools (IT)

Choice of tools and machining parameters, data sharing by the use of network, searching data, etc.

Tool Guide, Iscar Tool Advisor

The Proposed OADT Implementation Procedures

Implementation of OADT requires the proper procedures (see Fig. 3). Approach I is the standard procedure, which is proposed at an early stage of an SMME's existence. If OADT cannot be implemented in the specific manufacturing environment of the SMME, traditional TPP (without the use of digital solutions) may be developed. While the SMME grows and money savings exist (e.g. they can be used for new investments), additional possibilities should be considered (Approach II). However, these possibili-

Cost-related Implementation of the Digital Tools in SMMEs

The cost of digital tools (CDT) is the sum of machine and technological equipment costs (CMTE), software costs (CS), Internet tools' costs (CIT) and devices' costs (CD):

𝑪 𝑫𝑻 = 𝑪 𝑴𝑻𝑬 + 𝑪 𝑺 + 𝑪 𝑰𝑻 + 𝑪 𝑫 (1)
Usually, in the case of manufacturing firms, the number of DT increases as the firm grows. Fig. 4 presents this in the case of a small enterprise (SE), a medium enterprise (ME) and a large enterprise (LE). It is caused by the increased expectations from customers and also internal developments. The increase in the number of DT may cause an increase in the use of OADT. Which tools their employees should use is usually the decision of managers, but very often nowadays employees may decide how to enhance their work efforts by the use of new open access applications. It is a win-win situation in the case of SMMEs, which do not define rigid procedures, for both employees and employers. The employer does not need to invest additional funds, and the employee can contribute to the firm's growth by using OADT. Because of the increased number of DT in the case of a larger company, the expenses (DT expenses) may also increase. The expenses should be balanced by the proper income, in order to obtain adequate profit. In the case of SMMEs, the cost of preparation of a technological process can be defined as high. In this context, the technological process can be understood as a product, which costs less if mass-produced.

On the basis of the abovementioned discussion, it can be stated that, in the case of SMMEs, a reduction in expenses regarding technological process planning may be realized by the use of open access digital tools, even if the total number of existing digital tools is limited. Moreover, added value may be generated if the development process is also focused on OADT capabilities. Fig. 5 presents the challenges (aims) of SMMEs regarding the implementation of digital tools. It can be stated that the main challenges concern reducing expenses, automation, data sharing, training designed for employees, distance work, new investments and investors, co-funding capabilities and promotion. Next to the abovementioned aims, Fig. 5 presents the groups of digital tools which can be useful in achieving the specified aims. The groups of products which can be offered by SMMEs are also presented. In this context, manufacturing firms can offer digital data, sub-contracting, service and other products.

Conclusion

SMMEs usually need additional resources for investments. The use of DT, available as commercial licenses, is possible while SMMEs develop and gather the funds. At the early stage of SMMEs' existence, they need the support from open access digital tools (OADT). OADT have functional limitations but are offered for free or at a relatively low price and can be researched for the specific needs of the SMME. This study is a concise analysis of OADT. The OADT could be applied by SMMEs using the proposed implementation procedures, also by taking into account the presented capabilities.

Fig. 1 .

 1 Fig. 1. Classification of the groups of digital tools used in TPP.

Fig. 2 .

 2 Fig. 2. The development of TPP applications and possible future scenario.

  ties require detailed risk calculations and cost-effective investments in short-term software rent, license purchase or subcontracting of the SMME's contracts by other companies. The further development of the SMME may lead to the final purchase of commercial DTs. However, the development of used OADT at an early stage may also be sufficient to meet all the main requirements of the SMME without investing in commercial software in the future. This concept generates added value by promoting concentration on innovations in the area of OADT development in the SMME (e.g. investments in new functions of existing OADT or new open access tools).

Fig. 3 .

 3 Fig. 3. The approaches of OADT application in TPP of SMMEs.

Fig. 4 .

 4 Fig. 4. Comparison of selected and expected DT-related indicators in small (SE), medium (ME) and large (LE) enterprises.

Fig. 5 .

 5 Fig. 5. Challenges of CNC machine tools-based SMMEs and products offered with groups of digital tools used for specific aims (refer to Fig. 1).

Table 1

 1 mode both as stand-alone licenses on PCs, smartphones, tablets, and as Internet tools. Usually their functionalities are limited, compared to commercial software (i.e. CNC programming is limited to 2 or 2.5 axes, 3D modelling is less effective or impossible, etc.), but they can be considered the first choice if the existing budget is used for the purchase of machine tools and technological equipment. In the case of commercial software used by large/developed companies, it is reasonable to use the software offered by one producer, taking into account the fast training of employees. However, the bankruptcy of a producer will cause additional expenses for a large company. If SMMEs want to use open access tools, they must arrange a list of digital tools, which consist of the products of different companies. Developing the proper training procedures will protect an SMME against unwanted expenditure.As the conclusion of this chapter it should be stated that OADT find wider application in SMMEs than in large enterprises, but it could also be changed by increased (several times) R&D and dissemination of digital techniques.

presents the main groups of open access tools for TPP. Their main functionalities and examples are also presented. In the authors' opinion, the OADT are limited to Software (S) and Internet tools (IT). However, SMMEs must invest funds in Devices (D) and Machine tools/technological equipment (MTE). It can be observed that different CAD digital tools, CAM digital tools and digital calculators (i.e. enabling machining parameters' selection and verification) are available in open access

Table 1 .

 1 The main groups and examples of OADT for TPP in SMMEs.

			Main functionalities	Examples
	(S) Software tools	CAD CAM	2D drawing preparation, viewing of 2D or 3D models, measurements of dimensions, printing, changing a file format, copying or pasting CAD data, error correction, etc. Basic 2-, 2.5-or 3-axis machining, simula-tion of tool paths, basic postprocessing capa-bilities, etc.	For PC: DraftSight, FreeCAD, Libre CAD For Mobile Devices: CAD Reader, CAD Assis-tant

Acknowledgement

This study was developed within the project, "Science internship for the investigations of digitalization in manufacturing", financed by the Polish National Agency for Academic Exchange (www.nawa.gov.pl) in the Bekker Programme.


Minchul Lee
email: minchul.lee@nist.gov

Serm Kulvatunyou

Design-for-Cost -An Approach for Distributed Manufacturing Cost Estimation

Keywords: Cost Estimation, New Product Introduction, Cost Breakdown Approach, Design-for-Cost, Supply Chain Management

Research has shown that design changes cost more in later stages of product development. Therefore, companies adopt Design-for-X methods to optimize product designs for many aspects in the early design stage. Despite such efforts, products often encounter several design changes during the commission of the production, a principal reason being failure to meet target costs. Accurately estimating cost in the early design stage is difficult due to insufficient information. In particular, as production becomes more distributed cost estimation is also more difficult because information is more distributed. This paper introduces a cost estimation method to address this problem. It describes a distributed manufacturing situation and a cost breakdown framework. A use case is provided to illustrate how the framework allows for supply-chain cost negotiation and design adjustments in the early design stage.

Introduction

Research has shown that design changes cost more in later stages of product development [START_REF] Ulrich | Product Design and Development[END_REF]. Therefore, companies adopt Design-for-X methods to optimize the product design from many aspects such as quality, time to delivery and cost in the early design stage. Companies have strong interest in the ability to accurately estimate manufacturing cost earlier in the design stage, because executives typically focus on maximizing profit.

In the New Product Introduction (NPI) process, companies typically set target market, volume, price, and manufacturing cost along with the design and functions of a product at the first stage [START_REF] Riitta | Something Old, Something New: A Longitudinal Study of Search Behavior and New Product Introduction[END_REF]. They are very interested in maintaining the profit margin; therefore, target manufacturing costs are validated at every NPI stage. Despite this general practice, unexpected costs usually shows up at the commission of production because the current cost estimation approach is insufficient for today's distributed manufacturing environment.

Traditionally, manufacturers 1 have most of the information necessary for cost estimation. It is not the case for distributed manufacturing. Manufacturers need to interact with suppliers, estimate component costs, and consider delivery and packaging costs from the early design stage. Cost estimation approaches for distributed manufacturing need to enable manufacturers to negotiate with suppliers and come up with supply chain strategies that reduce cost. For example, in addition to the typical design adjustments to reduce material and manufacturing process costs, manufacturers may find suppliers who can use the same material and purchase the material on behalf of all the suppliers to receive a larger bulk-buying discount. Without proper cost break down in the estimation, manufacturers will have difficulty negotiating with suppliers as they do not know which cost elements can be reduced.

This paper proposes a framework for manufacturing cost estimation in the early design stage for the case of distributed manufacturing. The framework reduces risks of encountering unexpected cost at the commission of production that could result in a costly design change. The rest of paper is organized as follows. First, a literature review on manufacturing cost estimation is given. Then, our cost estimation framework is outlined. Finally, a case study showing cost estimation of a supplied component is illustrated followed by a conclusion and future work.

Literature Review

In this chapter, a summary of existing manufacturing cost estimation methods and cost elements is provided.

Cost Estimation Methods

Manufacturing cost estimation methods can generally be divided into two groups: qualitative methods and quantitative methods [START_REF] Jian | Product Cost Estimation: Technique Classification and Methodology Review[END_REF].

Qualitative methods are based on a comparative analysis between a new product and similar products manufactured previously. On the other hand, quantitative methods are based on a detailed cost analysis of product design, its features, and corresponding manufacturing processes instead of simply relying on the past data or tribal knowledge of an estimator. The qualitative methods do not provide a cost break down that can be used to understand cost elements that benefit from design change and cost negotiation.

There are several types of quantitative methods including Operation-based [START_REF] Shehab | Manufacturing Cost Modeling for Concurrent Product Development[END_REF], Feature-based [START_REF] Zhang | Feature-Based Cost Estimation for Packaging Products Using Neural Networks[END_REF] and Break-down approaches [START_REF] Son | A Cost Estimation Model for Advanced Manufacturing Systems[END_REF]. Operation-based approach mainly estimates cost in terms of types of operations and considers material cost, factory expenses and manufacturing processing cost as part of the costs associated with the time of performing operations. This approach focuses on accurate estimation of the manufacturing processing cost but provides less detailed consideration on other cost elements.

The feature-based cost estimation approach identifies cost-related features of the design and estimates their costs. However, existing approaches in this category only consider conventional machining process.

The break-down approach partitions manufacturing costs into cost elements. An estimation is applied to each cost element. The estimated manufacturing cost is a sum of all cost elements incurred during the production cycle. Cost elements include material cost, manufacturing process cost, maintenance cost, and repair cost. Other cost elements are insurance cost [START_REF] Son | A Cost Estimation Model for Advanced Manufacturing Systems[END_REF], overhead costs [START_REF] Bernet | An Integrated Cost and Consolidation Model for Commingled Yarn Based Composites[END_REF], and calculated manufacturing process cost based on the hourly usage of machinery [START_REF] Ostwald | Engineering Cost Estimating[END_REF].

For more accurate cost estimation, we adopt a cost break-down approach. However, our research focuses on estimating cost for distributed manufacturing. For that, we have to look into what data is available for the manufacturer, and use it to extend the scope of cost elements such as packaging and delivery cost. The scope of this paper and associated cost elements is described in 2.2.

Cost Elements

According to [START_REF]Management and Cost Accounting[END_REF], selling price consists of machining, material, labor, indirect, selling and administrative expense and profit as shown in Fig. 1.

Fig. 1. Elements of Cost Most research focuses on

Final Factory Cost, however, it is necessary to widen the scope to Selling Price to include packaging and delivery cost in the Total Cost to Sell in order to estimate cost of a distributed manufacturer. To enable detailed cost analysis, cost elements for machining, tool, and defects should be added to Prime Cost. In conclusion, cost elements for distributed manufacturing are shown in Fig. 2.

Fig. 2. Cost Elements for Distributed Manufacturing

Cost Estimation Framework

In this chapter, we introduce a cost estimation framework for a supplied component. We assume that design of the component starts after target market, volume, and price are set as they are needed for the cost estimation. Several efforts provided logic (cost elements and formula) for estimating costs. However, they can be difficult to apply in the early design stage of distributed manufacturing because data are not available. Therefore, it is necessary to incrementally increase the accuracy of cost estimation as shown in Fig. 3 -starting from using logic requiring the least information and add more details and data as they become available. The approaches used to update the logic and improve the accuracy include further breaking cost elements, decomposing parameters in formula, and collecting additional data and updating the database. Ways to collect data include investigating trends in the market, prototyping the component internally, contacting the supplier, contacting the equipment makers, and contacting raw material suppliers.

Fig. 3. Cost Estimation Framework for Distributed Manufacturing

As shown in Fig. 2, the Selling Price of the supplier (or supplied component) needs to be estimated. Therefore, the cost of the designed part is estimated by considering the material, the manufacturing process, the packaging method, and the anticipated supplier, and then comparing with the target cost. The right-hand side of Fig. 3 shows which cost elements (in Fig. 2) are related to cost estimation steps.

In the early stages of development, since every aspect of the design is not decided, such as the raw material or manufacturing method, assumptions have to be made on the parameters of cost elements. Therefore, only as the design of the product becomes more concrete, the cost can be estimated more accurately.

Cost Estimation Method and Cost Breakdown

In this chapter, we introduce a basic formula for each cost element and discuss their essential aspects. The formula can be adjusted for different supplied components and situations. Each of the cost elements is estimated per a part.

Material Cost

Most of the research suggests estimating material cost using unit cost and amount. A basic estimation formula for material cost Cmat is given by

Material Cost Cmat = mt × Umat, ( 1
)
where mt is the total amount of the raw material and Umat is the unit cost for raw material However, the amount of material should be broken down into the net material, mn, and loss material, ml, to present an opportunity to reduce the lost material. The design engineer may improve the design or the manufacturer may work with supplier to improve the process to reduce the material loss. Materials are lost, for example, from preheating in the injection molding process and from chips or scraps in NC machining. Thus, material cost Cmat, which characterizes material loss is, given by:

Material Cost Cmat = ( mn+ml ) × Umat. ( 2
)

Machining, Labor and Tool Costs

Generally, the basic estimation formula for machining is expressed as the product of machine rate and machining time [START_REF] Son | A Cost Estimation Model for Advanced Manufacturing Systems[END_REF].

Machining Cost Cmcn = rm × tm, ( 3
)
where rm is the machine rate and tm is the machining time. Machine rate is usually calculated by dividing the cost to operate a machine or machines for the needed processing duration by the duration itself. Details of machine rate estimation is beyond the scope of this paper, but it can be calculated approximately with depreciation, electricity and maintenance cost. Machining time can be subdivided into setup time (ts) operation time(to) and nonoperation (idle time and down time) time (tno) [START_REF] Jung | Manufacturing Cost Estimation for Machined Parts Based on Manufacturing Features[END_REF].

Machining Cost Cmcn = rm × (ts + to + tno). (4)

Labor cost which is similar to machining cost is given by

Labor Cost Cl = rl × tl, ( 5
)
where rl is the labor rate and tl is the labor operation time. A labor rate is the cost of labor that is used to derive the costs of various activities or products directly related to manufacturing. Calculating accurate labor costs can be difficult for distributed manufacturing because every supplier has different wages and compensation. Average labor rate of the industry can be used and is a good reference point for negotiation. The average labor operation time can be obtained by dividing the total number of products made during the day by the work time per day. However, it is also possible to apply the technique of analyzing the operation time such as Modular Arrangement of Predetermined Time Standards (MODAPTS) [11]. Tool cost, the cost of devices such as a mold or a jig, is calculated by dividing the price of the device by the target production quantity.

Tool Cost Ctool = 𝑇𝑜𝑜𝑙𝑠 𝑃𝑟𝑖𝑐𝑒𝑠 𝑇𝑎𝑟𝑔𝑒𝑡 𝑝𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑜𝑛 𝑣𝑜𝑙𝑢𝑚𝑒 . ( 6
)

Defect Cost

As every manufacturing process has failures, defect cost needs to be considered. Defect rate is difficult to predict before production starts and it can vary between suppliers. Therefore, the defect rate is typically set to the same as or lower than an average of historical defect rates. The formula to predict the defect cost, Cdft, is given by

Defect Cost Cdft = (Cmat + Cmcn + Cl ) × rd, ( 7
)
where rd is the defect rate.

Packaging Cost

Packaging cost includes all the materials and processes needed for delivering the supplied component to the manufacturer.

Packaging Cost Cpkg = pb ÷ np + rl × tp, ( 8
)
where pb is the cost for package box, np is the number of parts for a box and tp is the packing time.

Delivery Cost

Delivery cost is the cost of delivering the packaged product from the supplier to the manufacturer. The method of estimating cost may vary depending on the transportation, but the formula below includes essential parameters for cost reduction analysis.

Delivery Cost Cdlv = rd × d ÷ nt ÷ np, ( 9
)
where rd is the delivery rate, d is the distance from supplier to buyer and nt is the number of boxes in a transport.

Selling and Administrative Cost and Profit

These cost elements can be estimated as percentage over other cost elements. Selling and administrative (S&A) cost also includes research and development. These ratios vary by industries and suppliers and may be obtained from industry statistics such as in [START_REF]Macro trend[END_REF][START_REF] Consultant | [END_REF]. The S&A ratio can be higher for a supplier with an R&D center, such as a PCB manufacturer. In addition, the profit ratio can be negotiable, and it may increase for favorable partners such as those delivering consistent quality components and on-time delivery. The basic formulas for the two cost elements are:

Selling and Administrative Cost Csac = (Cmat + Cmcn + Cl + Cpkg + Cdlv) × rsac (10)

Profit Cprf = (Cmat + Cmcn + Cl + Cpkg + Cdlv + Csac) × rprf, ( 11
)
where rsac is the selling and administrative ratio and rprf is the profit ratio.

Use case

The use case is a scenario in which a refrigerator manufacturer needs injection-molding parts from a supplier. After the supplier receives the drawings, molds are manufactured.

After the injection-molding part production and quality inspections are completed, they are put in protective tapes and delivered in boxes. Below, we discuss cost estimations and cost reductions experienced in this use case.

The component used 120g of abs resin; therefore, according to (1):

Material Cost Cmat = mt (120g) × Umat ($2.7/kg) = $0.324. However, it was determined that if two parts were produced at the same time in a single mold, machining, labor, and tool costs were reduced despite an increase in material cost. The increase in material cost came from additional resin needed for the sprue and runner (6g) in the mold. Hence, material cost using ( 2) is:

Material Cost Cmat = mt (120g + 6g) × Umat ($2.7/kg) = $0.340. The price of the mold to produce the product is about $30,000 and can be used 200,000 times. However, since we produce two parts in one mold, Tool Cost Ctool = $300,000 200,000 ÷ 2 = $0.75.

Machine rate for injection is calculated to be $24 per hour and the machining time is 40 seconds for two pieces.

Machining Cost Cmcn = rm($24/hour) × tmcn (40s / 2) = $0.13. The operator extracts the injected parts from the machine and performs visual inspection. The labor rate is $30 per hour and has the same tact time as machining time. And finally, the total cost is a summation of all the cost elements: $1.85 per part. Discussion: If the estimated cost cannot meet the target cost, it is necessary to consider changing to cheaper raw materials, increasing the number of parts in the mold, or changing the packaging method to reduce the cost.

The estimated costs cannot be guaranteed to be available from the supplier but can be negotiated with supplier based on the estimation. For example, it may be possible to negotiate the defect rate, S&A rate, etc.; or to come up with a supply chain strategy such as bulk buying raw materials at a lower price on behalf of multiple suppliers, arrange logistics services, etc.

Conclusion

In this paper, we introduced a method to estimate costs for distribute manufacturing components. In order to estimate the cost of distribute manufacturing, selling price should be considered instead of final factory cost. We also introduced cost elements of the selling price for a more accurate cost estimation. Through this, a framework is proposed that incrementally enhance the detail of cost estimation during the design process. A use case illustrated the cost estimation process for a component and discussed how parameters identified in cost elements could be used for adjusting design and negotiating with suppliers. Future work includes integrating the quantitative cost estimation approaches described in this paper with qualitative approaches. In this way machining time and working time can be better predicted based on data kept in a database of cost estimation. Therefore, defining data schema for such database is an important research topic.

Disclaimer

Any mention of commercial products is for information only; it does not imply recommendation or endorsement by NIST.

In this paper, manufacturers refer to the organizations that design and/or produce products or components that need subassemblies from supplier organizations. The two roles are played by organizations in a distributed manufacturing chain. For example, prime contractor (a manufacturer) designs and produces cars which require instrument panel assembly from a subcontractor (a supplier). A subcontractor on the other hand can be a manufacturer ordering electrical harness from a supplier.


Eiji Morinaga
email: morinaga@mapse.eng.osaka-u.ac.jp

Komei Iwasaki

Hidefumi Wakamatsu
email: wakamatu@mapse.eng.osaka-u.ac.jp

Eiji Arai
email: arai@mapse.eng.osaka-u.ac.jp

Reduction of Computational Load in Robust Facility Layout Planning Considering Temporal Production Efficiency

Most researches of facility layout planning (FLP) have aimed at finding a layout with which evaluation indices based on distance are minimized. Because temporal efficiency has not been considered in this stage but in post stages, the resultant temporal efficiency may not be optimal enough. The authors have developed an FLP method considering temporal efficiency, in which facility layout is optimized using genetic algorithm (GA), and have enhanced it so that robustness against changes in production environment can be taken into consideration. However, the enhanced method involves a large computational load, since numerous production scenarios need to be considered. This paper provides a method for reducing computational load in the robust FLP based on the sampling approach where each layout plan is evaluated with only a limited number of production scenarios in the optimization process by GA. Numerical experiments showed the potential of the proposed method to efficient robust FLP considering temporal efficiency.

Introduction

Facility layout planning (FLP) is an important stage of manufacturing system design and has been actively discussed for a long time [START_REF] Drira | Facility layout problems: a survey[END_REF][START_REF] Hosseini-Nasab | Classification of facility layout problems: a review study[END_REF]. In this research field, evaluation indices based on distance such as total travel distance and total material handling cost have been mainly considered, and sigle/multi objective optimization based on those indices have been performed by mathematical optimization (quadratic assignment problem [START_REF] Lawler | The quadratic assignment problem[END_REF] and mixed integer programming [START_REF] Sherali | Enhanced model formulations for optimal facility layout[END_REF]) or metaheuristics (simulated annealing [START_REF] Chwif | A solution to the facility layout problem using simulated annealing[END_REF], genetic algorithm [START_REF] Gonçalves | A biased random-key genetic algorithm for the unequal area facility layout problem[END_REF], ant-colony optimization [START_REF] Pour | Solving the facility and layout and location problem by ant-colony optimization-meta heuristic[END_REF], etc.). Those indices do not include temporal efficiency, which is considered in detail in the stage of production management performed after the manufacturing system design. This may result in unsuitable optimization from the point of view of the whole system. Some research groups have discussed FLP considering temporal efficiency [START_REF] Fujihara | A facility layout method linked to production scheduling[END_REF][START_REF] Hino | Resource reallocation based on production scheduling (1st report)[END_REF]. But, they dealt with allocation of facilities to pregiven sites, and did not consider detailed position and size of facilities. We proposed an integrated method of FLP and production scheduling in which the integrated planning problem was formulated as a mixed integer programming which aims to minimize makespan and includes detailed position and size of facilities as decision variables [START_REF] Morinaga | Facility layout planning with continuous representation considering temporal efficiency[END_REF]. We also proposed an FLP method considering detailed transportation routes and loading/unloading points of facilities in addition to temporal efficiency [START_REF] Morinaga | A facility layout planning method considering routing and temporal efficiency[END_REF]. Due to difficulty in describing the shortest transportation route by linear equations/inequalities mathematically, genetic algorithm (GA) was used for optimization of facility layout plan in which finding the optimal routes and calculating makespan based on the routes were performed for each layout plan.

Those methods aim at finding the optimal layout plan for a fixed production scenario. However, production conditions change dynamically in actual manufacturing, and the layout plan is no longer optimal after the production conditions changed. An approach for coping with this problem is to adopt the concept of robustness against the changes [START_REF] Moslemipour | A review of intelligent approaches for designing dynamic and robust layouts in flexible manufacturing systems[END_REF] and robust FLP has been discussed based on enumerative method [START_REF] Rosenblatt | A robustness approach to facilities design[END_REF], branch-and-bound method [START_REF] Kouvelis | Algorithms for robust single and multiple period layout planning for manufacturing systems[END_REF], and so on. The integrated FLP method was also enhanced from this point of view [START_REF] Morinaga | A robust facility layout planning method considering temporal efficiency[END_REF]. In the enhanced method, sum of product of the probability of production scenario and makespan achieved under the scenario was defined as an evaluation index for taking robustness into consideration, and optimization based on this index was performed using GA. This method, however, involves a lot of computational load, since production scheduling for calculating makespan needs to be performed for every possible production scenario. This paper describes improvement of this robust FLP method for reducing computational load.

Outline of robust FLP Considering Temporal Efficiency

This section provides an outline of the robust FLP method considering temporal efficiency [START_REF] Morinaga | A robust facility layout planning method considering temporal efficiency[END_REF]. This method is intended to be applied to job-shop production of J kinds of jobs with F facilities. The j(∈ {1, . . . , J})-th kind of job needs O j operations. Operation o ∈ {1, . . . , O j } of the j-th kind of job is processed by facility f jo ∈ {1, . . . , F }. Width and depth of facility f are w f and d f . Each facility has loading and unloading points. Those facilities are located in the production area which has a rectangular shape of width W and depth D.

Because of difficulty in describing the shortest transportation route by linear equations/inequalities mathematically, optimization of facility layout plan is carried out iteratively by GA and routing and calculation of temporal efficiency are performed for each layout plan (Fig. 1). For representing a layout plan as an the layout plan is then performed for every production scenario. This process has to be performed many times and therefore should be completed in a short time. For this reason, the simulation approach using a dispatching rule is taken for this process. After performing this process for all the scenarios, the layout plan is then evaluated by the following index:

Σ S s=1 M s ( Π J j=1 p s j ) (1)
where M s is the makespan of the schedule generated with the s-th one in the given set of production scenarios S the cardinality of which is S, and p s j is the probability of the number of production of the j-th kind of job specified by the s-th scenario, which is defined as a combination of the number of production.

Improvement for Reducing Computational Load

As for the robust FLP method stated in Section 2, the total number S of production scenarios to be considered increases exponentially as the number of the kind of jobs J increases, and therefore reduction of computational load in the optimization process is crucial. In the research field of stochastic programming, the sampling approach has been taken advantage of for reducing computational load in optimization, where only a limited number of scenarios are utilized for evaluation of an individual [START_REF] Yoshitomi | Genetic Algorithm in Uncertain Environments for Solving Stochastic Programming Problem[END_REF]. Because no theoretical validation had not been provided for this approach, Tokoro [START_REF] Tokoro | A statistical selection mechanism of GA for stochastic programming problems[END_REF] proposed a new GA in which individuals are evaluated based on mean fitness in some scenarios sampled at random and selection is performed using Welch's test, and reported the method was effective to obtain high quality solutions for stochastic facility location problems in a very short computation time. It is expected that this GA is also effective to reduce the computational load in the robust FLP method.

Figure 3 shows the improved robust FLP method using the new GA. As stated in the previous section, production scenario is defined as a combination of the number of production of jobs in this research. In this improved method, it is assumed that the number of production of each kind of job follows a normal distribution defined in a pre-given range, and a pre-fixed number (denoted by Ŝ) of production scenarios are sampled by determining the number of production with a normal random number in the range.

Production scheduling is then performed for each of the selected scenarios, and the mean of makespans among the scenarios M n g is calculated as follows.

M n g := Σ s∈ Ŝg M n s / Ŝ, (2)
where n and g are the ordinal number of the individual (i.e. layout plan) and the current generation, and Ŝg stand for the set of the selected scenarios. Then, the history data of each individual is updated as follows:

g n = g n + 1 (3) Mn = Mn + (M n g -Mn )/g n ( 4
)
σ 2 n = (g n -2)(M n g -Mn ) 2 /g n + (g n -2)σ 2 n /(g n -1), (5)
where g n represents how many generations the n-th individual has survived, and Mn and σ n stand for the mean and the standard deviation of the index (2) among the generations.

Because the new evaluation index ( 2) is calculated with the makespans obtained with a limited number of scenarios, a large value of the index does not always mean that the individual is bad and selection based on the index value of the current generation may be inappropriate. For this reason, Welch's test is utilized for determining whether an individual should be eliminated from the population. In this method, the statistic t n defined by

t n := ( Mn * -Mn )/ √ σ 2 n * /g n * + σ 2 n /g n (6)
is calculated for each individual and choose the set of individual D := {n| t n > t α (µ)}, where n * is the ordinal number of the best individual in the current generation, t α (µ) stands for the t-ratio with µ degrees of freedom and the confidence interval of α% confidence level and µ is the integer which is closest to

µ ′ := (σ 2 n * /g n * + σ 2 n /g n ) 2 / { (σ 2 n * /g n * ) 2 g n * -1 + (σ 2 n /g n ) 2 g n -1 } . ( 7
)
A pre-given number of the individuals in this set D are eliminated from the current population, since they can be regarded as significantly-bad individuals. The same number of new individuals are then generated by performing crossover to a pair of individuals which does not belong to D, and all the other individuals are left to the next generation.

Numerical Experiments

Due to page limitation, this section briefly describes results of numerical experiments using an example of F = 7, J = 5 and O j = 7. The range of the number of production of each kind of job was given as {4,5,6} (Case 1) and {2,3,4,5,6,7,8} (Case 2). In Case 1, as shown in Fig. 4, the index value of the layout plan obtained by the proposed method was worse than that of the conventional robust FLP, though computation time was reduced drastically to about 10%. It was also worse than the value of the layout plan obtained considering only the standard scenario. Figure 5 shows the result of Case 2, where it was impossible to obtain a layout plan by the conventional method due to its computational load (S = 16807). The index value obtained by the proposed method was better than at least that of the layout obtained considering only the standard scenario. Although it is necessary to perform optimization based on the conventional method and compare the index value of the result to this value of the proposed method in a future work, it is expected this result implies a potential of the proposed method to efficient robust FLP considering temporal efficiency.

Summary

This paper has described reduction of computational load of the robust FLP method considering temporal production efficiency. The GA based on the sampling approach and a statistical theory was applied to the robust FLP method. Numerical experiments implied the proposed method has a potential to efficient robust FLP considering temporal efficiency. Further numerical studies with much narrower and broader range of scenarios will be performed in a future work.

Fig. 1 .

 1 Fig. 1. Flowchart of robust FLP considering temporal efficiency.

Fig. 2 .

 2 Fig. 2. Chromosome representation of a layout plan [11, 15].

Fig. 3 .

 3 Fig. 3. Flowchart of the improved robust FLP method.

Fig. 4 .

 4 Fig. 4. Result of Case 1.

Fig. 5 .

 5 Fig. 5. Result of Case 2.

individual in GA, the production area is divided up into a grid of squares with sides unit length and each cell has its ID number. A layout plan is represented as an individual by defining the structure of chromosome as F pairs of two numbers, where the second number of the f -th pair is the number of clockwise rotation by 90 degrees, and the first number is the ID number of the cell on which the upper left part of facility f (Fig.2).For a layout plan represented by this method, the shortest route between any two facilities is obtained by using Dijkstra's method and required time for transportation between the facilities is calculated by dividing the length of the route by the velocity of an automated guided vehicle. Production scheduling for


Drs Roman Senderek

Jan Kuntz

Prof Volker Stich

Jana Frank

Service Engineering Models: History and Present-Day Requirements

Keywords: Smart services, service engineering, digitalization

Since the field of service engineering emerged in the late 20 th century, the service industry has undergone drastic changes. Among the reasons for these changes is the increasing digitalization, which has made it difficult for companies to successfully develop new service offerings. While numerous service engineering models are available to provide guidance during the design of new services, many of them cannot keep up with the requirements of today's economic environment. The present paper examines the requirements that service engineering models need to meet in order to be suitable guidelines for the digital age. To this end, the introduction illustrates how digitalization has changed the service industry. Afterwards, selected service engineering models and related norms are presented. Finally, a set of requirements for modern service engineering models derived from best practices from recent years is introduced.

Introduction

Since the 1990s, service engineering has established itself as a systematic process for the development of services. As a strategic and creative process that aims at designing and implementing services and individualized solutions in a model-based way, service engineering is to services what product planning and development are to physical products. Among the overarching goals of service engineering are an efficient service development and a high level of service quality. Therefore, service engineering promises a competitive advantage and an increase in quality and customer satisfaction [START_REF] Schuh | Service Engineering[END_REF][START_REF] Richter | Erfolg durch neue Services: Service Design & Engineering[END_REF]. Various service engineering models and related norms have been published over the course of the past decades. Service engineering models aim at supporting companies in developing successful service offers by providing a course of action that companies can follow in the development process, and many of them have proven their value for the development of services in the past. However, the service industry has changed significantly during the last years. One major factor contributing to this change is the ongoing digitalization, which has created a variety of new challenges. It has drastically changed the way services are created and delivered [START_REF] Höckmayr | Digitalisierung als Treiber für Faktenbasiertes Service-Systems-Engineering[END_REF][START_REF] Acatech | Smart Service Welt. Digitale Serviceplattformen -Praxiserfahrungen aus der Industrie. Best Practices[END_REF], and lower barriers of entry have paved the way for stronger competition and an overall increased supply [5]. Companies must therefore differentiate themselves from competitors by continuously delivering innovative solutions that speak to the individual customer needs.

As an effective tool to achieve this goal, digital services and smart services in particular have gained significant importance in recent years. Smart services can be defined as individualized combinations of physical and digital services that generate added value for providers and customers and offer demand-oriented value via digital platforms. They are based on smart products, which are connected to the internet, interact with their environment and gather environmental data. The collected data sets are combined with other easily accessible information and processed into so-called smart data, based on which smart services are designed [START_REF] Acatech | Smart Service Welt[END_REF]. In this digitalized economic environment, many companies struggle to develop successful digital services. This is partly caused by the lack of service engineering methods that are suited for this task [7], as many old models lack the flexibility that is required to keep up with the dynamic of today's market. The increasing share of digital components in service engineering reveals deficits in the direct application of classical service engineering methods to smart services. Thus, the development of smart services requires a new service engineering process that can quickly adapt to evolving customer needs, is efficient, requires little resources and is centered on the customer and the value it can create through data insights [START_REF] Leimeister | Dienstleistungsengineering und -management[END_REF]9]. The present paper suggests a list of requirements that service engineering models need to fulfill to succeed in today's economic environment. Before these requirements are presented, however, the following section provides an overview of selected service engineering models and norms that touch upon the topic of service engineering.

Selected service engineering models and related norms

Numerous service engineering models have been published in the past. They prescribe a course of action that serves as a guideline for the development of new services and usually consists of phases that represent a high-level outline of how the model is structured and describe an overarching goal for each stage of the model. Each phase can comprise various activities that describe the individual tasks the company needs to complete to fulfill the phase's goal. [START_REF] Gudergan | Service engineering. Multiperspective and interdisciplinary framework for new solution design[END_REF] There are three main types of service engineering models. The first type is the linear model in which each development phase builds on the previous one. While these models benefit from their simplicity and transparency, their one-track direction leads to a lack of flexibility and adaptability. The second model type is the iterative model, in which the individual development phases are meant to be repeated several times. With each iteration, a finer concept of the service is developed. This approach offers quick results and a flexibility in correcting mistakes; however, maintaining an overview of tasks fulfilled requires a high level of coordination. The third type is the prototyping model, which focuses on the early development of prototypes that can be tested with customers and improved based on customer feedback. Prototyping ensures a strong customer orientation although it demands a high level of complex coordination among all parties in order to function properly. [7, [START_REF] Richter | Service Engineering. Neue Services erfolgreich gestalten und umsetzen[END_REF][START_REF]Service Engineering. Entwicklung und Gestaltung innovativer Dienstleistungen[END_REF] Apart from service engineering models, numerous norms touch upon the subject of service engineering directly or indirectly. Table 1 provides a short overview of German and international norms related to the service engineering process and of selected service engineering models. Due to the limited scope of this paper, these cannot be explained in detail. For further information, please refer to the sources listed in Table 1. Many of these norms and models have proven to be valuable aids for the development of services in the past. However, a majority of the available models are not compatible with today's market situation, as they are too complex and require excessive resources and development time before initial results can be produced and tested for value [START_REF] Leimeister | Dienstleistungsengineering und -management[END_REF]. They are often inflexible and thus unsuitable for most fields of applications today [START_REF] Meiren | Derivation of a service typology and its implications for new service development[END_REF]. Researchers and companies alike agree that existing models must catch up with the new requirements for creating innovative service solutions. The next section explores what requirements a model needs to fulfill in order to be suitable for today's market. 3

Requirements for modern service engineering models

As explained above, many service engineering models are no longer suitable for today's economic environment as they lack agility and flexibility, which calls for the development of new service engineering methods. In order to identify requirements for service engineering models for the digital age, it seems sensible to have a look at methods that have recently proven their utility and success in practice and to find out what characteristics they share. While no all-encompassing recipe for service engineering has emerged yet, certain methodologies have established their worth in adding value out of a specific focal point. An analysis of recommendations from recent literature and trends in service engineering reveals three main best practices that exhibit proven results in various industries. These will be discussed in the following paragraphs.

The first best practice found in several recent and successful service engineering models is user centricity. While a clear focus on customer needs has always been important in service engineering, it has gained importance in the digital age as customers have become more empowered through a greater selection of increasingly individualized products. In the sense of customer centricity, an offering is created by integrating the user into the entire development process and thus co-creating a positive customer experience. The closer a user is involved in the development process, the more the offering will reflect their needs. Customer ideas can be used to create a first prototype, which is then presented to the user for testing and feedback. This is repeated in as many iterations as needed until the prototype and user expectations are matched. This approach requires an extensive collection and analysis of data concerning customer satisfaction and experience, but it also allows for a high degree of customization. [START_REF] Meyer | Understanding Customer Experience[END_REF][START_REF]What is digital economy? Unicorns, transformation and the internet of things[END_REF][START_REF]From touchpoints to journeys: Seeing the world as customers do[END_REF] The second best practice is the utilization of service ecosystems. Services are normally not developed and implemented by a company alone but through a collaboration of a multitude of actors and resources. Service ecosystems can be defined as "relatively self-contained, self-adjusting system[s] of mostly loosely coupled social and economic (resource-integrating) actors connected by shared institutional logics and mutual value creation through service exchange" [START_REF] Lusch | Service innovation: A service-dominant logic perspective[END_REF]. They provide a fertile environment for companies to innovate and realize challenging ideas as they enable them to partner with actors that can complement and expand their own resources. Today, digital infrastructures allow for more diverse actors and more resources to be integrated in a service ecosystem, which cultivates value co-creation on an immense scale. In a service ecosystem, all actors should be empowered by gaining access to the various ecosystem assets and infrastructures. That way, companies can tap into a wealth of resources that a service design model can develop into an innovative service offering. [START_REF] Acatech | Smart Service Welt. Digitale Serviceplattformen -Praxiserfahrungen aus der Industrie. Best Practices[END_REF][START_REF] Lusch | Service innovation: A service-dominant logic perspective[END_REF][START_REF] Immonen | A service requirements engineering method for a digital service ecosystem[END_REF] The third best practice can be described by the term 'agile'. An agile mindset involves a quick and flexible development process, is customer centric and collaborative in nature as cross-functional teams are brought together. Moreover, it is output oriented and entails constant reflection on previous work to identify shortfalls. In addition, the agile mindset is efficient in its use of resources [START_REF] Lamberth-Cocca | Towards a Reference Model for Agile New Service Development Using the Example of E-Mobility Service Systems[END_REF][START_REF] Groll | The Agile Service Management Guide[END_REF] and enables the design process to be adapted to changing requirements at any time. Furthermore, it allows for a shorter time to market. One essential method is the development of a minimum viable product (MVP), which means that a new offering is created with the bare minimum of core features that enable sufficient interaction for constructive user feedback. The final product is completed after multiple iterations of the MVP feedback loop. Agile approaches Fig. 1. Requirements for modern service engineering models also tend to follow the lean mindset, which includes a reduction of waste and aims to achieve more with fewer resources, including time and information. [START_REF] Ries | The Lean Startup. How today's entrepreneurs use continuous innovation to create radically successful businesses[END_REF][START_REF] Blank | Why the lean start-up changes everything[END_REF] The requirements resulting from these best practices, their purpose and suggestions for their application can be found in Fig. 1. For further information, please refer to the references listed in the table [START_REF] Richter | Erfolg durch neue Services: Service Design & Engineering[END_REF]9,[START_REF] Lusch | Service innovation: A service-dominant logic perspective[END_REF][START_REF] Immonen | A service requirements engineering method for a digital service ecosystem[END_REF][START_REF] Lamberth-Cocca | Towards a Reference Model for Agile New Service Development Using the Example of E-Mobility Service Systems[END_REF][START_REF] Groll | The Agile Service Management Guide[END_REF][START_REF] Ries | The Lean Startup. How today's entrepreneurs use continuous innovation to create radically successful businesses[END_REF][START_REF] Blank | Why the lean start-up changes everything[END_REF][START_REF] Vargo | Service-dominant logic: Continuing the Evolution[END_REF][START_REF] Piller | Core capabilities of sustainable mass customization[END_REF][START_REF] Randhawa | Service innovation. A review of the literature[END_REF][START_REF] Piller | Firms, Users, and Innovation. An Interactive Model of Coupled Open Innovation[END_REF][START_REF] Maglio | Innovation and big data in smart service systems[END_REF][START_REF] Ismail | Exponential organizations. Why new organizations are ten times better, faster, and cheaper than yours (and what to do about it[END_REF][START_REF] Olsen | The lean product play book[END_REF][49][START_REF] Richter | Service Engineering. Neue Services erfolgreich gestalten und umsetzen[END_REF].

Outlook

Even though the present paper has argued that many existing service engineering models are no longer suitable for today's economic environment, it is worth mentioning that some promising models that incorporate the requirements listed in Fig. 1 have already been or are currently being developed. Among these are Smart Service Engineering [START_REF] Senderek | Smart service engineering. An agile approach to develop data-driven services[END_REF], Multilevel Service Design [START_REF] Patrício | Multilevel service design. From customer value constellation to service experience blueprinting[END_REF] , Design Thinking for Industrial Services [START_REF] Redlich | Design Thinking für das Service Engineering in kleinen und mittelständischen Unternehmen[END_REF] and Recombinant Service System Engineering [START_REF] Beverungen | Recombinant Service System Engineering[END_REF]. Whether these models will prove their success in practice in the long run remains to be seen.

Table 1 .

 1 Selected service engineering models and related norms

	Models	Related norms
	• Scheuing and Johnson's linear model (1989) [13,	• DIN Fachbericht 75 [20]
	14]	• ISO/IEC 15940:2013 [21]
	• Edvardsson and Olsson's linear model (1996) [15]	• DIN ISO 9004-2:1991 [22]
	• Ramaswamy's linear model (1997) [16]	• DIN PAS 1082 [23]
	• Jaschinski's iterative model (1998) [17]	• DIN PAS 1094 [24]
	• Liestman's iterative model (2002) [18]	• DIN PAS 1091 [25]
	• Bullinger and Schreiner's circular model (2006)	• DIN PAS 1014 [26]
	[19]	• DIN PAS 1018 [27]
	• Cernavin's linear model (2007)	• DIN PAS 1019 [28]
	• Meyer and Böttcher's approach (2011) [9]	• DIN PAS 1047 [29]
	• Leimeister's model (2012) [8]	• DIN SPEC 91310 [30]
	• Roth's approach (2017)	• DIN PAS 1076 [31]
	• Pöppelbuß and Durst's Smart Service Canvas
	(2017)

Acknowledgment

This research and development project project Digivation constitutes the superordinate project of the funding line Service Innovation Based on Digitization and is funded by the German Federal Ministry of Education and Research within the research program Innovations for Tomorrow's Production, Services and Work under the registration number 02K14A221. The author is responsible for the contents of this publication.