Joshua Gross

Thorsten Wuest
email: thwuest@mail.wvu.edu

Smart Products in Smart Manufacturing Systems: An Opportunity to Utilize AR?

Keywords: AR, Augmented Reality, Smart Glasses, Smart Manufacturing, Smart Product, Industry 4.0

The evolution of manufacturing throughout time has allowed our industrial world to develop and ever faster create various products in large numbers using a myriad of industrial manufacturing processes. However, like everything in the world, time moves forward and the evolution does not stop but accelerate. Therefore, the implementation of Augmented Reality (AR) through the use of the internet of things, low cost sensors, and free programming languages, are all contributing factors to further advanced the industry. These implementations will allow for faster data processing, quicker error recovery time, loss and error prevention, a wider range of employees at different skill levels to be productive in a manufacturing environment. The implementation of an Arduino based sensor system into the manufacturing process enables data streaming to an AR device. This system allows for instant data streaming and visualization to the user/operator with information pertaining to the status of the product ('state'), key developments, and changes in the product/or system, any associated errors, and the best way to proceed with resolving them. The AR enabled system with its capability to streaming relevant information prevents losses associated with product quality issues and/or process down-time among a myriad of other applications. Preliminary research shows the ability to collect data from the product in addition to data processing on an external device to later be used on an augmented reality device.

Introduction

The ongoing fourth industrial revolution is enabled by data, data processing, and connectivity mainly through the internet of things [START_REF] Schlick | Cyber-physical systems in factory automation-Towards the 4th industrial revolution[END_REF]. Digitalization of data and processes aims to decrease production time, prevent errors and mistakes, and facilitates rapid designing and prototyping of one-of-a-kind products [START_REF] Ong | Augmented reality applications in manufacturing: a survey[END_REF]. The emergence of smart manufacturing will allow for all of these to occur while enabling individuals and companies to oversee their entire manufacturing process. This monitoring of the process enables this to be done in high detail, remotely, and with minimal effort. Real-time in-situ data collection and analysis is key to the future of manufacturing and this project aims to contribute to the progress in this field through an example of a novel in-situ data collection application using smart products during the production process itself. As technology further develops, systems such as the one explained in this paper can collect data in real-time and then be used in a visualization method, such as AR. [START_REF] Zheng | Smart manufacturing systems for Industry 4.0: Conceptual framework, scenarios, and future perspectives[END_REF]. The majority of smart manufacturing systems collect data of the to-be-manufactured part via sensor systems integrated in the machine tool and/or assembly line [START_REF] Mittal | Smart manufacturing: characteristics, technologies and enabling factors[END_REF]. In this case, the to-be-manufactured smart product collects the manufacturing process data via a system integrated sensor system. The idea is that the smart product collects data of the manufacturing process in real-time, and the insights from the analyzed data is provided to the operator as decision support via an Augmented Reality (AR) application. The integrated sensor system allows for rapid and specific data analysis pertaining to individual to-be-manufactured products by shop floor employees. In large scale manufacturing processes, product errors and production line down time can cost companies large amounts of money [START_REF] Salonen | Downtime costing-attitudes in swedish manufacturing industry[END_REF]. Having manufacturing floor employees overseeing the manufacturing process and products with AR is expected to contribute to the prevention and resolution of product associated errors [5,[START_REF] Uva | Evaluating the effectiveness of spatial augmented reality in smart manufacturing: a solution for manual working stations[END_REF]. The aim of this research is to demonstrate the application of AR in combination with a smart-connected product within the manufacturing process and show that the AR integration allows for rapid product data observance in addition to error resolution via a test-bed cyber-physical production system. Our experimental setup and development of the Arduino based sensor system is detailed in the following, touching upon how it is utilized in order to achieve data collection, analysis, and user feedback. The research is conducted in a test-bed comprised of a FESTO Didactics cyber-physical laboratory (CP-lab) (see Fig. 1). The eight-stage smart manufacturing system manufactures a simulated cell phone case that consists of two separate plastic parts that are manipulated and assembled in a fully automated manufacturing process. This data and analysis allows us to instantly track the progress and working state of a single product in order to give the operator instant feedback.

However, this test-bed application can be theoretically applied to a much larger scale in many different scenarios such as automotive part monitoring, machine and part maintenance, and quality control of products. The data processing combined with the AR applications allows the operator to instantly and easily read out all of the pertinent information regarding the part that they are inquiring into.

The paper is structured as follows: in section 2, a brief overview of the background and state of the art in smart product based process monitoring and AR applications in manufacturing is provided. Following, the research methodology is depicted in section 3 before initial results are presented in section 4. The future applications of the setup are discussed in section 5 before section 6 concludes the paper and provides a short outlook on future research.

Background and State of the Art

AR offers significant potential with regard to improving the overall manufacturing system and its processes [START_REF] Nee | Augmented reality applications in design and manufacturing[END_REF]. Currently, AR is being utilized in the manufacturing process to improve quality control, maintenance, and assembly. Within the last three years, AR has been directly applied to the following industries: medical, military, robotics, and manufacturing [START_REF] Ong | Augmented reality applications in manufacturing: a survey[END_REF]. Porsche, for example, is actively using AR to ensure that manufactured parts meet the design expectations and dimensions. By simply looking at a part, the AR glasses will "instantly gauge the dimensional accuracy, surface finish, tolerances and interference and other potential issues" [6]. Continually, Boeing uses AR to assist manufacturing floor employees with the electrical components installation to ensure accuracy and decrease installation time. Furthermore, the use of AR in this application also ensures the employee are not missing any components [START_REF] Rathbone | Real world applications of Augmented Reality (AR) in manufacturing[END_REF].

Another identified current AR application in manufacturing is utilized by ThyssenKrupp, a large industrial company among others in the elevator industry. AR is used to scan a staircase to measure all dimensions which are instantly sent to the manufacturing department. Furthermore, the AR allows the customer to see what the stair lift will look like after installation [6]. In the healthcare industry, GE Healthcare is using AR for "projecting the work instructions onto the parts and use sensors to monitor the assembly and give feedback to the operator" which inherently increases accuracy and efficiency [START_REF] Kellner | How Augmented Reality Improves Manufacturing Productivity[END_REF]. With the implementation of AR, "activities, such as design, planning, machining, etc., are done right the first time without the need for subsequent re-works and modifications" and therefore enables error prevention [START_REF] Ong | Augmented reality applications in manufacturing: a survey[END_REF]. Cyber-Physical systems such as the laboratory set-up used in this study enable a more accurate manufacturing process through the internet of things as well as AR. While a one-of-a-kind part is being produced, "the part is checked and verified against product design data in particular its dimensions and tolerances" which ensures that the part is being created to the proper specifications [START_REF] Anderl | Industrie 4.0-advanced engineering of smart products and smart production[END_REF]. Furthermore, Anderl states, "as products or components of products are based on cyber-physical systems their smart sensors are able to deliver data about the products' or the components' condition". If this data is sent to an AR system for analysis, the worker can utilize the information in real-time and "predictive maintenance can be taken" [START_REF] Anderl | Industrie 4.0-advanced engineering of smart products and smart production[END_REF]. AR would allow any worker to observe the part during the manufacturing process while being shown how the part being produced compares to the design and specifications. Continually, the construction industry is currently utilizing AR "to address defects that might be overlooked in the inspection process" [START_REF] Behzadi | Using Augmented and Virtual Reality Technology in the Construction Industry[END_REF]. This same technology can be applied to the manufacturing industry as a way to constantly monitor the part being produced. This constant observance in addition to the data display through AR will therefore prevent errors as well as help correct any existing ones. These examples of current AR applications demonstrate its potential when being fully implemented into the manufacturing process at all stages to improve efficiency, accuracy, and safety.

The current laboratory set up used for this study includes an Arduino Nano based sensor system which allows for high customization to collect data on various attributes of the product during production. As of now, the sensor system is capable of tracking acceleration and magnetic field, has a toggle switch for tracking mode, and a SD card reading to store all of the collected data. Figure 2 shows a CAD rendition of the mock cell phone with the Arduino Nano and associated sensors used to collect said data.

Fig. 2. Arduino Nano Sensor System CAD Layout

Due to limitations within the Arduino Nano and space constraints within the product's volume, the data is currently not processed in real-time. Off-the-shelf sensors are easier to use and deploy but have high physical and programmatic complications when it comes to customization of the solution. A custom-made Arduino based sensor system allows for high customizability at a low cost.

The implementation of the sensor system into the CP-lab system allows for customized data tracking throughout the entire manufacturing process whereas most current industries have the ability to strictly track physical location or status. For example, our sensor system in the CP-lab system will allow the user to see how the part is oriented, which station it is at, the temperature, and current progress through the manufacturing process whereas current tracking methods would simply read out how far along the part is in the manufacturing process. Table 1 shows a comparison of different sensor systems used for tracking in industry. This table compares barcodes/data matrices, radio frequency identification (RFID), off-the-shelf sensors (OTS), and our Arduino sensor system. Barcodes or data matrices are patterns that encode data that can be read by a scanner therefore showing the user the encoded data. RFID tags send data regarding a product through the use of radio waves and an antenna. If these tags have their own power source, they are categorized as being 'active', otherwise they are 'passive'. The OTS sensor is available to anyone and can be purchased online and comes with capabilities determined by the manufacturer. Based on this table it is clear that the Arduino system has the most potential and capability. The boxes that are marked with a '(x)' indicates a sort of caveat. Regarding the passive RFID, this has the ability to locate if and only if it is connected to the machine itself. In its own, the passive RFID does not have the ability to locate items. The OTS sensor is customizable to a certain degree. The OTS can be customized within the spectrum that the manufacturer allows. Outside of the guidelines set by the manufacturer, the OTS cannot be customized.

Case Study of Smart Manufacturing Testbed

The Arduino Nano based sensor system is directly placed inside the beginning half of the product. As the product goes through the different stages of placement, drilling, laser measuring, turning, heating, compression, and sorting, data is collected and placed onto the SD card. The data is in the form of an Excel file which is then imported into Python. In Python, the data can be processed in practically any fashion before continuing. Once the raw data has been processed and analyzed, it is then embedded into an Android compatible graphic user interface (GUI) such as Kivy, where is it made tangible and can be interacted with. This GUI is what presents the data in a user-friendly fashion to the user and data can be sorted and organized. Due to the Android compatibility, the GUI can be compressed into an installer executable and placed onto the Android based AR glasses where the user will directly and most practically be interacting with the processed data. Figure 3 illustrates the principle structure of the project and how all of the steps lead to enabling better decision making for the user, the manufacturing engineer, on whether or not to take action.

Fig. 3. Theoretical Project Work Flow

Due to the nature of Arduino single board computer systems, the sensor system is highly customizable and can be changed easily to collect different data types. Furthermore, Arduinos allow for the pre-processing of data such as ignoring certain factors, actively looking for outliers or false data, or searching for anticipated data. Having 'clean' data coming out of the sensor system enables faster, easier, and more accurate results and feedback to come out of the Python data processing code.

Results

The results so far reflect the current stage of development of the ongoing research project. At this point, the smart-connected product is capable of actively and reliably collecting data during the manufacturing process. The acquired sensor data contains multiple sensor measurements and visualization of the data enables an analysis of the smartconnected products progress and changing state through the multi-stage mart manufacturing system. However, the wireless connectivity to the AR glasses and/or network is still development. While the Arduino system provides several advantages (see Table 1), it is bulkier and difficult to fully integrate in the product itself. Therefore, certain adjustments to the manufacturing process itself were necessary. Some findings at this stage include: struggles in data transfer between devices, inability to access certain features on the AR glasses, and the inability to extract useful data off of the CP-lab system. However, the sensors system has been able to collect data containing elapsed time, 3-axis acceleration, orientation, and the magnetic field. This data is compiled into an MS Excel sheet on an SD card to be used for data processing. Note that this data flow is not wireless due to limitations with the Arduino Nano which is not Bluetooth capable at the moment. Furthermore, using the current sensor system makes it difficult to collect data while in the CP-lab system as the wiring interferes with the laser sensors thus sending a false signal to the system and therefore making a faulty part.

Due to challenges faced in multiple stages of our ongoing research project, there is limited tangible data available as of this point in time. Other limitations include unexpected barriers, such as ability to gain developer access on the AR glasses thus limiting testing ability with the connection between the data and the glasses. Continually, this prevents the installation of any custom made software to be placed onto the glasses and therefore the data processing code cannot be directly utilized on the glasses. Another limitation includes the lack of available space inside of the manufactured part which limits the number and size of sensors to be used.

Conclusions and Future Work

The initial results of this project show promise with regard to providing a richer data picture seamlessly to the operator via AR. However, there are several challenges that need to be addressed to further the research. Certain obstacles can be overcome relatively quickly whereas others will take a lot more time and resources. For example, redesigning and developing the sensors system will prevent the interference with the laser measuring system and therefore prevent false signals to be sent to the CP-lab system, a relatively easy solution. However, other issues such as the lack of access to the AR glasses and lack of functionality in the glasses cannot simply be solved and require more resources and collaboration with the manufacturer of the AR equipment. Continuing research regarding the implementation of AR into the manufacturing process offers high potential for a case study in addition to real-world applications. Actively contributing to Industry 4.0 and Smart Manufacturing will continue to grow the field in addition to further advance and optimize the manufacturing process thus increasing productivity, efficiency, safety, and saving money. Refinement of the sensor array enables the opportunity for highly customized data tracking and therefore detailed feedback to the user. RFID tags are currently used in many industries but are limited in their tracking capability. The use of AR will allow for tracking throughout the entire manufacturing process and therefore potentially prevent recalls, prevent full process shut downs, and potentially save parts that have encountered manufacturing errors. Regarding industries that do not have any sort of tracking, a system like this could revolutionize their company. Being able to track the parts your company is producing offers many benefits and allows a company to utilize the collected data and become more efficient and productive in their manufacturing.

The utilization of the Arduino based sensor system allows for high customization. Many off-the-shelf sensors are non-customizable and therefore you either collect all of the data that they're capable of collecting or you don't use the sensor at all. Our system allows for the user or company to decide which data they want to collect and which data is negligible to them. This reduces the data processing time and therefore gives the user more in-depth feedback in less time.

Fig. 1 .

 1 Fig. 1. Testbed setup -CPlab Smart Manufacturing System

Table 1 .

 1 Comparing popular tracking technologies with regard to data processing capabilities

		Barcode /	RFID	RFID		Arduino Sensor Sys-
		Data Matrix	(Passive)	(Active)	OTS	tem
	Identify	x	x	x	x	x
	Identify + Locate	x	(x)	x	x	x
	Identify + Locate + Sense	-	-	x	x	x
	Identify + Locate
	+ Sense + Com-	-	-	-	x	x
	municate
	Identify + Locate
	+ Sense + Com-municate + Pro-	-	-	-	-	x
	cess Data
	Customizable	-	-	-	(x)	x

Acknowledgements

This work was supported by the J. Wayne & Kathy Richards Faculty Fellowship in Engineering at West Virginia University and the Research Apprenticeship Program (RAP) at the WVU Office of Undergraduate Research.


Marina Zizakov

Stana Vasic
email: vasic.stana@uns.ac.rs

Milan Delic

Marko Orosnjak

Srdjan Vulanovic

The Interdependencies of Quality Management, Knowledge Management and Innovation Performance. A Literature Review

Keywords: Quality management, Knowledge management, Innovation performance

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

Introduction and theoretical background

In the last two decades, quality management has been one of the most common approaches for improving quality and innovation performance [START_REF] Honarpour | Knowledge Management,Total Quality Management and Innovation: A New Look[END_REF]. Further, quality management is perceived as a philosophy that strives for continuous organizational improvement. Quality management can include establishing quality policies and quality objectives, and processes, to achieve these quality objectives through quality planning, quality assurance, quality control, and quality improvement [START_REF]ISO Online Browsing Platform[END_REF]. Accordingly, organizations with a higher level of quality management maturity may benefit from: higher product/service quality, increased customer and employee satisfaction, lowered costs, as well as improved financial and innovation performance. This implies that a high level of maturity in quality culture may foster positive effects on the organizational competitive advantage and innovational capabilities [START_REF] Prajogo | TQM and Innovation: A literature review and research framework[END_REF]. Moreover, it encourages the conquest of new and existing markets [START_REF] Leker | Open and closed Innovation -Different innovation cultures for different strategies[END_REF]. However, some studies claim the opposite. Namely, quality management does not always produce the expected results. Such findings contradict previously mentioned theoretical bases [START_REF] Abrunhosa | Are TQM principles Innovation in the Portuguese footwear industry?[END_REF], [START_REF] Hoang | The impact of total quality management on Innovation: Findings from a developing country[END_REF], [START_REF] Perdomo-Ortiz | Total quality management as a forerunner of business innovation capability[END_REF], [START_REF] Singh | Relationship between TQM and Innovation: an empirical study[END_REF], [START_REF] Manders | ISO 9001 and product innovation: A literature review and research framework[END_REF], [START_REF] Shan | The mediating effect of Innovation between total quality management (TQM) and business performance[END_REF]. Hence, it could be argued that power of quality management, in creating an adequate environment and culture of supporting innovations, is still a subject of debate by many authors [START_REF] Perdomo-Ortiz | Total quality management as a forerunner of business innovation capability[END_REF], [START_REF] Manders | ISO 9001 and product innovation: A literature review and research framework[END_REF], [START_REF] Rafailidis | The mediating role of quality on the relationship between cultural ambidexterity and innovation performance[END_REF], [START_REF] Moreno-Luzon | TQM, Innovation and the role of cultural change[END_REF].

Subsequently, it is necessary to find a real cause of such contradictions, and, accordingly, to explore certain fostering organizational aspects, which should contribute to the positive impact of quality management on the innovation performance. Some authors propose that knowledge management might present the missing link between these two [START_REF] Honarpour | Knowledge Management,Total Quality Management and Innovation: A New Look[END_REF], [START_REF] Prajogo | TQM and Innovation: A literature review and research framework[END_REF], [START_REF] Zeng | The impact of hard and soft quality management on quality and innovation performance: An empirical study[END_REF], [START_REF] Hamdoun | Knowledge transfer and organizational innovation: impacts of quality and environmental management[END_REF], [START_REF] Hung | Knowledge as a facilitator for enhancing innovation performance through total quality management[END_REF].

The ISO 9001: 2015 puts a great emphasis on learning. Consequently, the organization should identify needs and trends and accordingly constantly improve the competence of employees [START_REF]ISO Online Browsing Platform[END_REF]. By involving employees in the improvement of the work process, assigning responsibilities and providing opportunities for training, positive effect on motivation, awareness and attitudes towards changes is created [START_REF] Hamdoun | Knowledge transfer and organizational innovation: impacts of quality and environmental management[END_REF], [START_REF] Asif | Knowledge creation through quality management[END_REF], [START_REF] Mokhlis | The impact of ISO 9001 Quality Management on organizational learning and Innovation: Proposal for a conceptual framework[END_REF], [START_REF] Ooi | Developing an integrated model of TQM and HRM on KM activities[END_REF]. Therefore, in this research, the importance of knowledge management will be considered, as a key mediator of a positive influence between quality management and innovation performance.

Knowledge management is the systematic management of an organization's knowledge assets to create value and meet tactical and strategic requirements [START_REF] Honarpour | Knowledge Management,Total Quality Management and Innovation: A New Look[END_REF], [START_REF] Hamdoun | Knowledge transfer and organizational innovation: impacts of quality and environmental management[END_REF], [START_REF] Hung | Knowledge as a facilitator for enhancing innovation performance through total quality management[END_REF], [START_REF] Yusr | Does Interaction between TQM Practices and Knowledge Management[END_REF], [START_REF] Drucker | Knowledge-Worker Productivity: The Biggest Challenge[END_REF]. Knowledge management could influence the adequate application of knowledge in processes, development of employee awareness, and quality culture [START_REF] Honarpour | Knowledge Management,Total Quality Management and Innovation: A New Look[END_REF], [START_REF] Hung | Knowledge as a facilitator for enhancing innovation performance through total quality management[END_REF], [START_REF] Yusr | Does Interaction between TQM Practices and Knowledge Management[END_REF], [START_REF] Jiménez-Jiménez | Implications of TQM in firm's innovation capability[END_REF]. Accordingly, many researchers have concluded that knowledge management should encourage the maintenance of existing and new knowledge, and, thus, contribute to the innovation performance, overall [START_REF] Honarpour | Knowledge Management,Total Quality Management and Innovation: A New Look[END_REF], [START_REF] Hamdoun | Knowledge transfer and organizational innovation: impacts of quality and environmental management[END_REF], [START_REF] Hung | Knowledge as a facilitator for enhancing innovation performance through total quality management[END_REF], [START_REF] Ooi | Developing an integrated model of TQM and HRM on KM activities[END_REF], [START_REF] Yusr | Does Interaction between TQM Practices and Knowledge Management[END_REF]. Innovation performance could be defined by new ideas or creativity to improve the products, processes, procedures that increase the significance, usefulness and performance of the products, services and process [START_REF] Hamdoun | Knowledge transfer and organizational innovation: impacts of quality and environmental management[END_REF], [START_REF] Hung | Knowledge as a facilitator for enhancing innovation performance through total quality management[END_REF], [START_REF] Dedy | An analysis of the impact of total quality management on employee performance with mediating role of process innovation[END_REF].

The question is, "What does quality management need to provide, to support innovation performance?" Further, it is also necessary to determine "How does knowledge management contribute to innovation performance?" and "How does knowledge management mediate the relationship between quality management and innovation performance?".

Methodology

The research methodology in this paper builds upon a Systematic literature review (SLR).

Firstly, the research questions were defined, according to the previously mentioned theoretical assumptions. These are:  RQ1: "What does quality management need to provide, to support innovation performance?"  RQ2: "How does knowledge management contribute to innovation performance?"  RQ3: "How does knowledge management mediate the relationship between quality management and innovation performance?" After that, we need to define research keywords in accordance with research questions. The keywords are defined as follows:

"QUALITY MANAGEMENT" OR "QUALITY MANAGEMENT SYSTEM" OR "*QM" OR "TOTAL QUALITY MANAGEMENT" OR "ISO 9001" AND "KNOWLEDGE MANAGEMENT" AND "INNOVATION" OR "INNOVATION PERFORMANCE"

These keywords were used to define search queries, within SCOPUS indexed database. The query has returned 108 papers.

To get more accurate results, the search query was refined. The first phase of the search was performed on titles, abstracts and keywords, to above-mentioned "inclusion criteria" (Table 1). The re-definition of search results has returned 42 papers, in total.

Then, papers were assessed by their title, abstract and their content. Some papers were found to be non-related to our scope of review. Thus, they were removed from further analysis. (i.e. "Non-related criteria" -Table 1.) Finally, based on NR exclusion criteria (Table 1.), out of 42, 20 papers were retained.

In the second phase, the authors removed seven more papers. The reason that papers were excluded from the further analysis is that they were not technically available through portals and index databases, as well as accessible for downloads. In overall, 13 papers were subdued for detail analysis.

Further, considering the forward and backward citation criteria, a total sum of 17 articles were added, increasing our final list of papers to 30.

Table 1. Inclusion and exclusion criteria I/E Criteria Sub-criteria Criteria Explanation Inclusion criteria

Full-text papers (FTP) Selected studies that are only abstracts, presentations or posters will not be included in the study.

Language (LAN)

Full text of the article must be written in English.

Time frame (TF)

Selected studies must be published between 2010-2020 to be included in the SLR Selected studies (SS)

Studies included journal articles, conference papers Subject area (SA) "Engineering", "Business, Management and Accounting", "Decision Sciences", "Social Sciences" Exclusion criteria Non-related (NR) NR1: Paper is not related to manufacturing organizations. NR2: Paper relates to health care organizations, biotechnology, pharmacy, hospital industry or aerospace industry. NR3: Paper relates to university and government.

Results and discussion

The results of the literature review are presented in this section. Regarding the type of analyzed papers, there are 25 academic journals (83%) and only 5 conference papers (17%), of which 23 are based on empirical research (questionnaire/interview/survey) (72%), and 9 on literature review and research framework (28%). The papers are mostly from 2010 (20%), 2017 (23%) and 2019 (23%), and slightly less from 2011 (7%), 2012 (10%), 2015 (4%), 2016 (9%) and 2018 (4%). The research originates mainly from Asia (52%), Europe (24%), Africa (10%), America (9%) and Australia (5%) and was conducted in the manufacturing industry. Only two papers come from Serbia and the wider region in a transitional economy.

The greatest focus of the literature review was on the identification of constructs, by which the examined factors were operationalized, and on the nature of the relationship between these factors (Table 2). Summarizing the results, we conclude that the factors of QM with the highest frequency are: customer focus (16), leadership ( 14), employee management [START_REF] Moreno-Luzon | TQM, Innovation and the role of cultural change[END_REF], and process approach [START_REF] Shan | The mediating effect of Innovation between total quality management (TQM) and business performance[END_REF]. Factors with slightly lower frequency are: continuous improvement (8), supplier management [START_REF] Perdomo-Ortiz | Total quality management as a forerunner of business innovation capability[END_REF], learning [START_REF] Perdomo-Ortiz | Total quality management as a forerunner of business innovation capability[END_REF], strategic and systematic approach (5), teamwork and quality chain (4), effective communications (and culture) (4), information (measurement) and analysis (4). In the case of knowledge management, factors that stand out the most are: knowledge application (6), knowledge dissemination (6), and knowledge acquisition (5), whereas knowledge transfer/sharing (4), knowledge creation (3), and knowledge storage (2) are factors with slightly lower frequency. While, for innovation performance, the factors are product and service innovation [START_REF] Hoang | The impact of total quality management on Innovation: Findings from a developing country[END_REF], and process innovation [START_REF] Hoang | The impact of total quality management on Innovation: Findings from a developing country[END_REF].

Based on this, the proposed research framework is presented in Fig. 1.

Fig. 1. The proposed research framework

When it comes to the relationship between factors, the vast majority of studies examine relationships between quality management and innovation performance. This is shown in Table 2. The nature of relationships between used constructs in these studies is also given (i.e. positive (+)/negative (-)/both (+/-)). The table includes papers in which knowledge management represents a mediating connection between quality management and innovation performance and five of these mediating effects are positive. Based on the literature review, it could be argued that, in most cases, quality management produces positive results on innovation performance, with the strong positive mediator influence of knowledge management. Also, the majority of shortcomings in a strong positive mediator relationship between quality management and innovation performance are related to the lack of human awareness and knowledge in quality culture (i.e. 5(+/-)) [START_REF] Honarpour | Knowledge Management,Total Quality Management and Innovation: A New Look[END_REF], [START_REF] Singh | Relationship between TQM and Innovation: an empirical study[END_REF], [START_REF] Shan | The mediating effect of Innovation between total quality management (TQM) and business performance[END_REF], [START_REF] Moreno-Luzon | TQM, Innovation and the role of cultural change[END_REF], [START_REF] Zeng | The impact of hard and soft quality management on quality and innovation performance: An empirical study[END_REF], [START_REF] Hamdoun | Knowledge transfer and organizational innovation: impacts of quality and environmental management[END_REF], [START_REF] Hung | Knowledge as a facilitator for enhancing innovation performance through total quality management[END_REF], [START_REF] Mokhlis | The impact of ISO 9001 Quality Management on organizational learning and Innovation: Proposal for a conceptual framework[END_REF], [START_REF] Yusr | Does Interaction between TQM Practices and Knowledge Management[END_REF], [START_REF] Dedy | An analysis of the impact of total quality management on employee performance with mediating role of process innovation[END_REF], [START_REF] Salim | The relationship between TQM practices and organisational innovation outcomes: Moderating and mediating the role of slack[END_REF].

Answer to RQ1: "What does quality management need to provide, to support innovation performance?"

Quality management should create a work environment that fosters creativity, motivates employees to be innovative thinkers and take risks, thus, enhancing innovation performance [START_REF] Hamdoun | Knowledge transfer and organizational innovation: impacts of quality and environmental management[END_REF], [START_REF] Mokhlis | The impact of ISO 9001 Quality Management on organizational learning and Innovation: Proposal for a conceptual framework[END_REF]. Leadership has primary responsibility for developing adequate climate, by setting innovational goals, on all organizational levels. Leadership must provide the necessary resources and establish an atmosphere of trust, teamwork, empowerment and people management. The autonomy of decisions and the idea of sharing knowledge among employees should lead to innovation [START_REF] Honarpour | Knowledge Management,Total Quality Management and Innovation: A New Look[END_REF], [START_REF] Perdomo-Ortiz | Total quality management as a forerunner of business innovation capability[END_REF], [START_REF] Zeng | The impact of hard and soft quality management on quality and innovation performance: An empirical study[END_REF], [START_REF] Hamdoun | Knowledge transfer and organizational innovation: impacts of quality and environmental management[END_REF], [START_REF] Mokhlis | The impact of ISO 9001 Quality Management on organizational learning and Innovation: Proposal for a conceptual framework[END_REF], [START_REF] Ershadi | Measuring the Impact of Soft and Hard Total Quality Management Factors on Customer Behavior Based on the Role of Innovation and Continuous Improvement[END_REF]. For achieving this, a higher level of employee awareness is necessary, where knowledge management plays a predominant role. This brings to the forefront the importance of learning climate among employees. Learning is a key factor in creating innovations and developing awareness [START_REF] Kovacevic | Knowledge management as critical issue for successful performance in digital environment[END_REF]. Thus, employees must continually acquire new knowledge and share it among themselves. Subsequently, the acquisition of knowledge may lead to the development of an adequate quality culture [START_REF] Honarpour | Knowledge Management,Total Quality Management and Innovation: A New Look[END_REF].

Answer to RQ2: "How does knowledge management contribute to innovation performance?"

Through the papers of the literature review, results show a positive relationship between knowledge management and innovation performance (Table 2.) Knowledge manage-ment can significantly contribute to the development of innovation performance, if organizations have the capacity to acquire, develop and share knowledge [START_REF] Hamdoun | Knowledge transfer and organizational innovation: impacts of quality and environmental management[END_REF]. Acquiring knowledge from inside and outside of the organization leads to knowledge modification of employees, and increasing creating values, including new product development and innovation performance enhancement [START_REF] Moreno-Luzon | TQM, Innovation and the role of cultural change[END_REF], [START_REF] Hung | Knowledge as a facilitator for enhancing innovation performance through total quality management[END_REF], [START_REF] Yusr | Does Interaction between TQM Practices and Knowledge Management[END_REF], [START_REF] Honarpour | Total quality management, knowledge management, and Innovation: an empirical study in R & D units[END_REF].

3.3

Answer to RQ3: "How does knowledge management mediate the relationship between quality management and innovation performance?"

Some authors recognize quality management to be a precursor of knowledge management, while others claim the opposite [START_REF] Hung | Knowledge as a facilitator for enhancing innovation performance through total quality management[END_REF], [START_REF] Honarpour | Total quality management, knowledge management, and Innovation: an empirical study in R & D units[END_REF]. Nevertheless, in both cases, they should have a positive impact on innovation performance [START_REF] Honarpour | Knowledge Management,Total Quality Management and Innovation: A New Look[END_REF], [START_REF] Prajogo | TQM and Innovation: A literature review and research framework[END_REF], [START_REF] Hung | Knowledge as a facilitator for enhancing innovation performance through total quality management[END_REF], [START_REF] Yusr | Does Interaction between TQM Practices and Knowledge Management[END_REF], [START_REF] Jiménez-Jiménez | Implications of TQM in firm's innovation capability[END_REF], [START_REF] Honarpour | Total quality management, knowledge management, and Innovation: an empirical study in R & D units[END_REF]. By implementing quality management, besides internal organizational benefits, organizations are also encouraged to improve relationships with suppliers and customers, which, eventually, leads to the development of organizational innovations [START_REF] Lalic | Open Innovation in developing regions: An empirical analysis across manufacturing companies[END_REF]. To achieve the expected results, they must acquire as much knowledge as possible about the needs and expectations of stakeholders, and, also, develop strong ties with them. This is where knowledge management has a predominant role. Also, to respond to users' needs and expectations on time, they need to expand their existing knowledge [START_REF] Brunswicker | Managing open innovation in SMEs: A good practice example of a german software firm[END_REF]. This underlines the purpose of knowledge management as well, and, at the same time, a contribution to innovations are, clearly evident. Moreover, many authors found that quality management practices facilitate the creation and expansion of knowledge within an organization [START_REF] Hung | Knowledge as a facilitator for enhancing innovation performance through total quality management[END_REF], [START_REF] Asif | Knowledge creation through quality management[END_REF], [START_REF] Mokhlis | The impact of ISO 9001 Quality Management on organizational learning and Innovation: Proposal for a conceptual framework[END_REF], [START_REF] Ooi | Developing an integrated model of TQM and HRM on KM activities[END_REF], [START_REF] Colurcio | TQM: A knowledge Enabler?[END_REF].

One of the basic principles of quality management is learning, employee involvement and teamwork. It is believed that through the application of knowledge management and quality management, simultaneously, companies can improve both innovation and efficiency. Consequently, the implementation of quality management should increase organizational efficiency and reduce costs [START_REF] Modarres | Impact of total quality management on organisational performance[END_REF], [START_REF] Donate | The role of knowledge-oriented leadership in knowledge management practices and Innovation[END_REF], [START_REF] Martínez-Costa | Does quality management foster or hinder Innovation? An empirical study of Spanish companies[END_REF]. In conclusion, organizations aiming to improve innovation performance should work on achieving the synergistic effect between knowledge and quality management.

Conclusion

We conclude the following, if the leadership is fully committed to quality management, involves employees and encourages them to acquire and create knowledge, transfer / share and storage knowledge through various trainings and motivations, in this case, quality management benefits innovation performance through knowledge management. Also, the shortcomings of the relationship between quality management and innovation performance are mostly caused by the lack of proper knowledge management among employees.

The main limitation of this study is the use of one database -Scopus. This leaves space for further research in other literature sources. More to say, there are only two studies on this subject, that were carried out in a transitional economy of Serbia and the wider region. Thus, future empirical research is planned to be conducted in this region. The lack of research data implies the need for further work, in resolving shortcomings of quality management, especially in relation to innovation performance.

Table 2 .

 2 The nature of relationships between quality management, knowledge management, in-

	novation performance (+ positive, -negative, +-both)
		(Total) Quality	Knowledge	Innovation
		management	management	Performance
	(Total) Quality management		14(+)	22(+), 1(-), 5(+/-)
	Knowledge management	8(+)		18(+)
	Innovation performance	2(+)


Simon Hummelshøj Sloth
email: ssloth16@student.aau.dk

Magnus Abildsten Bøgh

Konstantinos Christian Møller Nielsen

Panagiotis Konstantinidis

Inkyung Sung
email: inkyungsung@mp.aau.dk

Data-Driven Replenishment Method Choice in a Picking System

Keywords: Order picking, Continuous replenishment, Replenishment methods, Data-driven decision support, Principal component analysis

Designing the operating policies of picking systems, which connect inventory and production/assembly lines in a manufacturing system, involves determining replenishment methods for individual items. These replenishment methods affect the overall labor cost and flexibility of the picking system by determining the frequency and quantity of item picking. To design the replenishment method, in this paper, we propose a data-driven decision support framework that provides guidance in comprehending features in a picking system based on demand, size, and value of individual items. The proposed framework is then applied to a real-world case for validation.

Introduction

Many companies, especially manufacturers, have seen increasing pressures on cost reduction during the past decades. To cope with this, they often focus on efficient production processes to reduce the operating cost. Furthermore, manufacturing systems have been increasingly challenged by short product life cycles, frequent product introductions and ever changing product mix. These trends force manufacturing companies to focus on a flexible and responsive manufacturing system [START_REF] Wiendahl | Changeable manufacturingclassification, design and operation[END_REF].

One of the system elements contributing to the desired production and manufacturing systems is a picking system, which conducts the process of retrieving items from storage and fulfilling the order of internal or external customers. By nature, the picking system plays a key role as an interface between inventory and production. Importantly, the picking system accounts for a large part of operating costs related to inventory due to the labor-intensive nature of the activities involved in the system. In general, labor cost accounts for upwards of 55% of the total inventory operating cost [START_REF] Le-Duc | Travel distance estimation and storage zone optimization in a 2-block class-based storage strategy warehouse[END_REF], therefore, the cost for inventory operations can be reduced dramatically by designing an efficient picking system.

The complexity of picking system design is mainly determined by the target system characteristics. These include mechanization level of a system, information availability, scale of operations, and organizational/operating policies for storage, routing, batching and order release [START_REF] De Koster | Design and control of warehouse order picking: A literature review[END_REF]. In this paper, we focus on designing an operating policy especially for a replenishment method that governs overall picking processes. Specifically, a replenishment method specifies (1) how a picking order for a specific item is generated, (2) how the picking orders are batched, (3) how the items are picked, and (4) how the items are delivered.

A replenishment method is generally designed by following the conventional discrete order picking (COP) method or the continuous replenishment (CR) method. In the COP method, the items are picked to an order generated with a fixed time interval, whereas in the CR method, the items are continuously picked by re-order point logic following a pull type control mechanism [START_REF] De Koster | Design and control of warehouse order picking: A literature review[END_REF][START_REF] Karaesmen | A performance comparison of pull type control mechanisms for multi-stage manufacturing[END_REF][START_REF] Liberopoulos | A unified framework for pull control mechanisms in multi-stage manufacturing systems[END_REF][START_REF] Nash | Warehouse Order Picking Process at Pelco Products, Inc[END_REF]. While the CR method lowers picking labor cost and increases the picking flexibility in general [START_REF] Nash | Warehouse Order Picking Process at Pelco Products, Inc[END_REF], the performance of the methods varies depending on the characteristics of the item to be picked [START_REF] Goetschalckx | Classification and Design of Order Picking[END_REF].

Let us note that the replenishment method choice is not straightforward due to the complicated relationships involved in a picking system and picking operations. For example, human factors such as learning, fatigue and ergonomics [START_REF] Grosse | Incorporating human factors in order picking planning models: framework and research opportunities[END_REF] are critical to the choice but difficult to measure. The real-life conditions, e.g. safety constraints and picker blocking [START_REF] Van Gils | Designing efficient order picking systems: The effect of real-life features on the relationship among planning problems[END_REF], also increase the complexity of relevant planning problems including the replenishment method choice. As a result, an analytical framework for the replenishment method choice in a picking system has not yet materialized properly and the choice often relies on human expertise in practice.

To address the issue, we propose a data-driven replenishment method choice framework that guides a manager to identify a suitable replenishment method for an item, i.e. whether an item is replenished by the COP method or the CR method. Given a data set containing various items and their specifications, we first determine the attributes of the items that explain the data set, and classify the items based on their replenishment methods (COP or CR), resulting in two data clusters. Given an item of interest, closeness of the item to the clusters of the replenishment methods is evaluated with respect to the attributes determined and, finally, the corresponding replenishment method of the cluster with higher closeness to the item is proposed for the item.

Proposed Replenishment Method Choice Framework

In this section, the proposed framework is explained with connection to its implementation in a large Danish manufacturing company. The framework consists of six steps as visualized in Fig. 1.

Step 1 is to identify the attributes of items that would affect the replenishment method choice. Through interviews with the relevant decision-makers in the manufacturing company and considering the commonly available attributes of an item in the database of companies, we identify the following attributes of Fig. 1: Visualization of the replenishment method choice framework an item, which will be used to classify the replenishment method of the item: size, weight, price and demand.

For a physical item to be picked, it is critical to consider its size to evaluate if the item violates space constraints in the picking system. Similarly, the weight of an item is essential for the replenishment method choice as the total weight of items carried on a shelf or a pallet is limited. The size and weight of an item are also important as they are closely linked with ergonomic challenges for system operators.

Price is another attribute that affects the desired replenishment method for an item. Note that the CR method tends to pick an item in larger batches, allowing the following assembly or production process to stock the item for convenience. However, this practice could increase the risk of the item being lost or damaged during the stock period. Therefore, the COP approach that would reduce the batch size of the item will be suitable for an expensive and valuable item.

Last, the demand of an item, and in particular the demand rate of the item, are keys to sustain a flow of items in a system. Obviously, an item with low demands is not suitable to be managed by the CR method. We further consider the demand variation, which affects the safety stock level of an item.

It should be noted that the impact of the selected attributes of an item on the replenishment method choice would vary depending on the picking system of interest. To address this issue, further analysis can be performed to see how well the picking item data of the system is explained by the selected attributes.

For this purpose, the principal component analysis (PCA) can be used to evaluate whether the identified attributes are able to explain the variation between the replenishment method choices of items. The PCA is a tool for dimension reduction with the aim of reducing several variables into fewer principal components (PC) that captures a decreasing amount of the variation between the given observations [START_REF] James | An Introduction to Statistical Learning: with Applications in R. STS[END_REF].

A Danish manufacturing company investigates their choice of replenishment method for the items to be picked in relation to their internal logistics between inventories and several assembly lines. At the time of investigation, 1777 items and 274 items are being replenished by the COP and CR methods, respectively.

Considering the item attributes proposed in the framework, the company set the weight, price, yearly demand, and demand variation of an item as the attributes of interest for the replenishment method choice.

To verify the attributes identified, the PCA is performed on the data set of the items, which contains all the attribute values of the items covered in the picking system. The result shows that the selected attributes are able to explain the variation between the items. The PCA result is summarized in Table 1. The PC 1, which is the direction of the data where the most variation in the data is explained, is mainly contributed by the weight, price and the coefficient of variation (CV) of demand. In Step 2, after identifying the attributes, they are normalized to a range between 0 and 1 to eliminate the impact of units of measurement on the replenishment method choice.

In Step 3, weights can be applied to the attributes to scale the importance of them, thereby emphasizing parameters with high impacts on the replenishment method choice. The weights can be guided by the loading scores from the principal components of the PCA (see Table 1) or by the experience of a manager of the target picking system.

In Step 4, ideal candidate items, which represent items most suitable for the COP and CR methods, respectively, are found based on the current replenishment method choices for the items. One feasible way of finding the ideal candidate item is to examine the items that are currently replenished by the same method (COP or CR). Specifically, given the item cluster grouped based on their replenishment method choice, an item positioned in the center of the cluster can be considered as the ideal item for the method. Following the idea, with k item attributes, the centroid C of n items in a cluster can be calculated by

C = n i=1 x i1 n , n i=1 x i2 n , • • • , n i=1 x ik n , (1)
where x ik is the kth attribute value of item i.

The company groups all items in the picking system according to the replenishment method of the items (COP or CR) and the centroids of the groups are calculated accordingly. To visualize the grouping, the PCA result is re-called. Fig. 2 shows the plot where all items are presented with respect to the first and second PC of the analysis with a log-transformation applied for better visualization of data. In the figure, the items are color coded according to their methods and the vectors (arrows) represent the directions the values of the attributes are varied along.

Fig. 2: Log-transformed biplot of PC 1 and PC 2 with confidence ellipses containing 95% of observations

From the figure, it is first observed that in the picking system of the company an item with high demand, a low price, a low weight and a low CV tends to be managed by the CR method, aligned with common practice. Moreover, it is also observed that items with a PC 1 value greater then 0 are most likely managed by the COP method.

In Step 5, after identifying the ideal candidate item for a replenishment method in Step 4, the relevance of an item of interest to a replenishment method is measured. For measuring, the weighted Euclidean distance between the item of interest and the ideal item of the method (the centroid of the data cluster of the method) can be calculated by

w 1 • (x 1 -c 1 ) 2 + w 2 • (x 2 -c 2 ) 2 + • • • + w k • (x k -c k ) 2 , (2)
where w k is the weight of the kth attribute determined in Step 3, and x k and c k are the kth attribute value of the target item and the ideal item, respectively. In Step 6, based on the distance measured, the replenishment method of an item is determined. If the distance of an item to the centroid of a certain replenishment method is close enough, the item might can be replenished by the method.

Note that the replenishment method choice made based on the distance should be used as a guidance because the suitable replenishment method for an item would depend on picking system-and item-specific characteristics and other practical conditions. However, such a guidance dramatically lessens the burden of a manager, especially when there are many items and attributes of the items to consider for the replenishment method choice.

The distances of all the items in the picking system to the centroids of the COP and CR method clusters are measured by equation [START_REF] Le-Duc | Travel distance estimation and storage zone optimization in a 2-block class-based storage strategy warehouse[END_REF]. Based on the distances, 67 items, currently replenished by the COP method, are identified as potential items to be replenished by the CR method. From our analysis, it was estimated that replenishing the 67 items by the CR method would yield a reduction in the total number of picks by 3.8% for the three-month period assessed.

Concluding Remarks

The objective of this study is to support the replenishment method choice in a picking system, which is often done manually. For this, we designed a framework that investigates common attributes of the items replenished by the same method (steps 1-3) and suggests a replenishment method for an item based on its attribute values (steps 4-6). We believe that the proposed framework can provide evidence to validate the practice in the replenishment method choice in a picking system and further increase the automation level of decision-making.

Future research could include extending the framework to contain a predictive function, so that the framework is able to analyze the changes on items over time. This could be done by adding a time dimension to the attribute set of an item. Also, the possibility of extending the outcome of the proposed framework, i.e. supporting additional replenishment methods including the COP and CR methods, can be tested for the generalization of the proposed framework.

Table 1 :

 1 Cumulative variation explained by the principle components

	Attribute	PC 1 PC 2 PC 3 PC 4
	Yearly Demand	0.041 0.984 0.174 0.001
	Unit Price	-0.661 0.064 -0.201 -0.720
	Unit Weight	-0.641 0.085 -0.330 0.688
	CV	-0.388 -0.144 0.906 0.091
	Cumulative Proportion 0.513 0.764 0.968 1.000


Jon Halfdanarson
email: jon.halfdanarson@himolde.no

Nina Pereira Kvadsheim

Knowledge and Practices Towards Sustainability and Circular Economy Transitions: A Norwegian Manufacturing Perspective

Keywords: Sustainability, circular economy, sustainability knowledge and practices, manufacturing industry

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

Introduction

Competitive pressures, innovation and market potentials, regulatory concerns, and a sense of corporate social and environmental responsibility have prompted many companies to engage in sustainability issues in recent years [START_REF] Shrivastava | Transdisciplinary Study of Sustainable Enterprise[END_REF]. Sustainability is used here as a common notion referring to "development that meets the needs of the present without compromising the ability of future generations to meet their own needs" [2, p. 3].

From the business perspective, the circular economy (CE) is understood as a model that is "restorative and regenerative by design, and aims to keep products, components, and materials at their highest utility and value at all times" [3, p. 1]. In this study, CE is viewed as an approach to implement the much-discussed concept of sustainable development [START_REF] Kirchherr | Conceptualizing the circular economy: An analysis of 114 definitions[END_REF]. The sustainability and CE transitions are due to the many environmental problems (e.g. climate change, resource depletion), which are brought about by unsustainable consumption and production patterns [START_REF] Köhler | An agenda for sustainability transitions research: State of the art and future directions[END_REF] . In fact, to develop society and the corporate world towards a more sustainable state, an incremental continuous transition is needed, which according to [START_REF] Grin | Transitions to Sustainable Development: New Directions in the Study of Long Trem Transformative Change[END_REF] is the response to a number of persistent problems confronting today's modern societies. To be able to respond to these challenges, [START_REF] Grelland | Sustainability and Care: On a Philosophical Contribution to the Project of Sustainability[END_REF] advocates that we need the will to do it -a sense of urgency, a will that follows from an attitude of concern and we need to develop knowledge of sustainability/CE, which will then be transformed into sustainable patterns of human activities. Succinctly put, if companies have knowledge of sustainability and CE, then they should be able to understand what drives and blocks sustainability/CE transitions, and how such transitions can be accelerated [START_REF] Grin | Transitions to Sustainable Development: New Directions in the Study of Long Trem Transformative Change[END_REF]. This is consistent with Velenturf and Jopson [START_REF] Velenturf | Making the business case for resource recovery[END_REF], who argue that companies are willing to transition into sustainability and CE yet lack the knowledge and skills to transform their actions to meet sustainability challenges in a meaningful way. Indeed, for companies to act in an environmentally responsible way, they must know enough about environmental issues [START_REF] Kempton | Environmental Values in American Culture[END_REF]. However, numerous theoretical frameworks have been developed to explain this gap between the possession of knowledge and awareness related to sustainability and displaying behavior that consciously seeks to minimize the negative impact on the environment (e.g. minimize resource and energy consumption, use of non-toxic substances, reduce waste production). Although various studies have been undertaken, no definitive explanation has yet been found [START_REF] Kollmuss | Mind the Gap: Why do people act environmentally and what are the barriers to pro-environmental behavior?[END_REF].

Against this backdrop, this study aims to assess the knowledge and practices among manufacturing companies working towards transitioning into sustainability and the CE. This focus on manufacturing companies can be justified on the following grounds: Firstly, companies are endowed with resources and capabilities, as such they have the potential to drive the change towards a more sustainable economy [START_REF] Shrivastava | Transdisciplinary Study of Sustainable Enterprise[END_REF]. Secondly, they are the most influential institutions within the market, which is the most dominant coordinating institution in the world [START_REF] Angelis | Business Models in the Circular Economy: Concepts, Examples and Theory[END_REF] and thus any strategy aiming at increasing sustainability cannot be pursued without their involvement. Thirdly, those companies that dominate economic activity are increasingly viewed as a major cause of the current ecological crisis [START_REF] Porter | Creating Shared Value: How to reinvent capitalism and unleash a wave of innovation and growth[END_REF][START_REF] Schaltegger | Business Models for Sustainability: A Co-Evolutionary Analysis of Sustainable Entrepreneurship, Innovation, and Transformation[END_REF] and hence it is essential that they commit to being proactive in the transition towards a more sustainable economy [START_REF] Schaltegger | Business Models for Sustainability: A Co-Evolutionary Analysis of Sustainable Entrepreneurship, Innovation, and Transformation[END_REF][START_REF] Hahn | Beyond the Bounded Instrumentality in Current Corporate Sustainability Research: Toward an Inclusive Notion of Profitability[END_REF]. This study therefore contributes to burgeoning research on sustainability and CE in the manufacturing industry. By contextualizing it to the Norwegian manufacturing industry, it provides a novel counter-point company perspective on knowledge and practices towards sustainability/CE.

The paper proceeds as follows. Section 2 reviews literature related to knowledge and practices towards sustainability and the CE. Section 3 describes the method employed by the study, followed by analysis and discussion in section 4. Finally, Section 5 presents the closure and limitation of the study.

Knowledge and practices in sustainability and CE transitions

Sustainability transitions are long-term, multi-dimensional, and fundamental transformation processes through which established socio-technical systems shift to more sustainable modes of production and consumption [START_REF] Markard | Sustainability transitions: An emerging field of research and its prospects[END_REF]. However, there is little research on knowledge and practices related to sustainability and CE transitions. Extant literature focused on the gap between awareness and behavior; for instance, Blake [START_REF] Blake | Overcoming the 'value-action gap' in environmental policy: Tensions between national policy and local experience[END_REF] looked into the value-action gap in environmental policy, and on the external environmental pressure on firms and their behavior [START_REF] Liu | Investigating external environmental pressure on firms and their behavior in Yangtze River Delta of China[END_REF]. While Kollmuss and Agyeman [START_REF] Kollmuss | Mind the Gap: Why do people act environmentally and what are the barriers to pro-environmental behavior?[END_REF] found that increases in environmental knowledge and awareness generally did not lead to pro-environmental behavior. Regarding studies that focused on the awareness and behavior of firms toward developing a CE, Lin [START_REF] Lin | The conditions and institution innovation of manufacturefirms' development circular economy[END_REF] found increasing CE knowledge levels among industrial firms. However, as can be seen from the review of literature, despite the wealth of evidence concerning environmental awareness and the behavior of firms, there is little research on the knowledge and practices of firms in transitioning into sustainability and CE. Thus, this study seeks to contribute to knowledge for sustainability and CE transformation and should therefore be deemed essential.

In the subsequent section, the method employed in this study is presented.

Method

Considering the study's objective, a multiple-case-study approach was deemed appropriate since it is suitable for replication purposes [START_REF] Yin | Case Study Research and Applications : Design and Methods[END_REF]. In contrast to a single case study, a multiple-case-study approach increases external validity and reduces observer bias [START_REF] Voss | Case research in operations management[END_REF]. The case companies for this study were selected from an industrial cluster that comprises 50 companies from different industries (e.g. heavy machinery, IT, plastic, lighting system). Since it is typical in case study research to select cases by applying specific criteria, instead of selecting a random or stratified sample [START_REF] Eisenhardt | Making Fast Strategic Decisions in High Velocity Environments[END_REF], the following main selection criteria were applied in this study: [START_REF] Shrivastava | Transdisciplinary Study of Sustainable Enterprise[END_REF] if the company was a manufacturing company with either in-house or outsourced production, and (2) if the company was interested in exploring business opportunities under the sustainability logic, where the business model(s) is aligned with the triple bottom line. Data were collected through a survey, where 20 manufacturing companies with different market segments (e.g. advanced equipment to maritime/casting industry) responded to the self-administered questionnaires via Quest Back. The survey design was chosen as it is often the most direct and cost-effective way of soliciting the respondents' opinions about the subject of interest [START_REF] Hui | A reporting guideline for IS survey research[END_REF]. In order to gain an in-depth understanding of the companies' perspectives on sustainability/CE, semi-structured interviews were conducted with the CEO and departmental managers of eight of the 20 companies. To allow for data triangulation [START_REF] Yin | Case Study Research and Applications : Design and Methods[END_REF], these data sources were supplemented with document analysis of official documents such as sustainability/CE articles and company websites.

The next section presents the analysis and discussion of the study.

Knowledge and practices towards sustainability and CE

The study's results indicate that the companies have a relatively good general understanding about sustainability, have a positive view of it and a relatively strong willingness to act toward its development. Figure 1 shows different viewpoints of how companies understand sustainability. These definitions are categorized according to the similar elements identified in all the given definitions (20 in total). Here we can see that there is a strong focus on the long-term perspective and a strong awareness of the three dimensions of sustainability. For example, 30% of the companies report that sustainability is concerned with assuming that nature and the environment are not an inexhaustible resource and so, it is essential to protect them and use them rationally, by making sure that their businesses do not harm the environment. While 60% of the companies defined sustainability through the following interconnected pillars: environment, economic and social and the future generation. This definition corresponds to the one provided by the widely acknowledged Brundtland Report 'Our Common Future' describing it as "development which meets the needs of the present without compromising the ability of future generations to meet their own needs" [10, p. 43].

Figure 1: Different aggregations of interpretations of "Sustainability" and what that means to the companies

Considering the CE, the results indicate that most of the firms had heard of the CE (95%) and most of them could identify CE with regeneration and recycling of waste, minimization of resource consumption and waste production. This result corresponds to Lin et al's [START_REF] Lin | The conditions and institution innovation of manufacturefirms' development circular economy[END_REF] finding that most industrial firms have increasing CE knowledge levels.

Further, the survey reveals how the companies ranked how important different dimensions of sustainability are for them. Unsurprisingly, the economic aspect of sustainability is identified as the most important for all the studied companies. This is also evident in Gusmerotti et al's [START_REF] Gusmerotti | Drivers and approaches to the CE in manufacturing firms[END_REF] study, where the findings suggest that the economic drivers are the only effective lever for the transition towards more sustainability. On the other hand, social sustainability is ranked as more important than environmental sustainability. This result is somehow unexpected, especially bearing in mind that social sustainability is often overlooked relative to the other two aspects. What is more, from the in-depth interviews it shows that most of these companies under study are regarded as "cornerstone companies" and as such, they view the social aspect of their role in the region as very important. Consequently, these companies recognize the significance of their relationships with people, communities and society, and thus social responsibility becomes part of their core business strategy and they consider how their activities affect people. Ultimately, this might lead to unlocking new markets, becoming the source of innovation for new product lines, raising internal morale and employee engagement as well as helping retain and attract business partners, all of which are of vital importance for business competitiveness, survival and local community development [START_REF] Sundström | Implementing social sustainability for innovative industrial work environments[END_REF]. Additionally, the survey identifies the measures that the companies perceive as their best possibility to improve their work with environmental sustainability. As illustrated in Figure 2, influencing the upstream value chain by setting environmental requirements to purchased goods, is seen as the least promising area for action. While influencing customers by offering more sustainable products, is identified as the best measure of improving their sustainability efforts. This result is in line with the companies' prioritization of economic sustainability over the other aspects, since by influencing customers, they are placing the customers at the center of corporate strategy, which is based on the insight that customers are the basis of company profitability [START_REF] Gupta | Valuing customers[END_REF].

Figure 2: Best chances of improving the sustainability efforts of the company

Besides, interview data reveal the barriers that are most relevant for the companies under study when it comes to implementing sustainable business models (SBMs). These barriers are structured following three categories: 1) regulatory, 2) market and financial and 3) behavioral and social. The most frequently mentioned barrier is lack of economic incentive, followed by financial risk. This is due to the complex nature of SBMs and the typically higher initial cost and investment required for implementation [START_REF] Oghazi | Circular business model challenges and lessons learned-An industrial perspective[END_REF]. This implies that a great deal of advances in economic model innovation will need to be made if these companies are to effectively become sustainable.

The results also describe the actions currently being taken by the companies in how they work with sustainability. Figure 3 shows a range of different approaches among the companies, from having a central role in the company's strategy to having no particular focus on sustainability at all. In addition, the results indicate that approximately 60% of the companies already have plans of transitioning into a CE, 20% do not have any plans, whereas about 20% report that CE is irrelevant for them due to the complexity of their products (their products have a lifespan of 50 years and due to their material mix, it is impossible to reuse them after end of life). Moreover, the results show different ways on how the companies can approach CE (see Figure 4). Most of the companies (80%) report developing new markets and innovative business models, as one way of making a shift to a CE. This is in consonance with EMF [START_REF] Emf | Towards a CE: Business Rationale for an Accelerated Transition[END_REF], who assert that the shift to a CE requires innovative business models that either replace existing ones or seize new opportunities.

Figure 4: How the companies identify ways to approach the circular economy

What is more, the results show measures already taken by the companies in transitioning towards a CE and these include, among others, 1) cooperation with customers on return and upgrading solutions instead of delivering new products; 2) focusing more on recycling all of production waste not only the products and avoid producing "use and throw" products and 3) working by following CE principles (reduce, reuse and recycle) with as effective production as possible, and recirculating materials and products.

Closure and limitation

This study has sought to assess the knowledge and practices among manufacturing companies working towards transitioning into sustainability and the CE. The results present a picture that is quite interesting, even though not unexpected. The level of knowledge and practices regarding sustainability is generally high among the companies under study. This can partly be explained by the high level of requirement and regulation Norwegian industry is faced with, both domestic and internationally. The Norwegian business culture, where taking extended responsibility for social and environmental conditions, also play an important role. Thus, although the level of knowledge and practices seem fairly high, we see a difference in their perception of "sense of urgency". Some companies are focusing on only meeting minimum requirements, while others are inspired in doing morealthough not all know how to proceed to increase their effort. One important aspect concerning this industry's sustainability efforts is the ability to adapt to new regulations and requirements. From this study, it is clearly stated that the industry has an increased focus on sustainability from company ownership and management perspective, and that this focus is expected to increase in the years to come. However, this is not highlighted as a negative challenge, it is rather emphasized that more and stricter regulations and requirements on sustainability performance are welcomed, as this removes and excludes some of the competing companies that are not able to comply with this.

A limitation of the current study warrants attention as it addresses specific cases in a particular geography, one economy only, Norway and manufacturing industry. Hence, as a further avenue for research, we suggest in-depth case studies with other industries as well as other economies.

  consciousness, environmental and carbon footprinting That our business does not harm the environment Measures and activities that secures continued operation and competetiveness in a longer perspective That environmental, social and economic effects are factors that are considered and taken into account Todays decisions should not be made at the expense of future generations, in neither economic, environmental or social terms Number of companies

  more sustainable materials in production Influence customers -for instance by offering more sustainable productsInfluence the upstream value chain -by setting environmental requirements to purchased goods

Figure 3 :

 3 Figure 3: How does the company engage in sustainability?


Inger Gamme

Bjørn Andersen

Håkon Raabe
email: hakon.raabe@sintef.no

Daryl Powell

Value chain integration -a framework for assessment

Keywords: Value chain integration, Maturity assessment, Multiple case study. 1

Despite an abundance of research on the topic, firms continue to struggle with integrating their value chains in order to create and deliver more value to customers. Silo-thinking (rather than systems-thinking) is a typical symptom of poorly integrated value chains. In this paper, we explore the enablers of better value chain integration, before developing and presenting a framework that can be used for assessing the maturity of value chain integration in organizations. We draw on practical insights from a multiple case study of several diverse companies currently working with the systematic integration of their value chains.

Introduction

A greater level of value chain integration promises firms increased productivity, better quality performance, reduced costs, and a higher level of customer satisfaction [START_REF] Gamme | Assessing Integration in Value Chains-Assessing enablers and disablers of integration in different sectors[END_REF]. Yet organizations often struggle to integrate their value chains due to specific factors, such as the presence of a "silo-culture" as well as a lack of documentation or systematization [START_REF] Basnet | Nurturing internal supply chain integration[END_REF][START_REF] Pagell | Understanding the factors that enable and inhibit the integration of operations, purchasing and logistics[END_REF]. Defending silos over teamwork has recently emerged as a symptom of big company disease [START_REF] Ballé | The Lean Sensei. Go. See. Challenge[END_REF]. Moreover, having little flexibility in written descriptions and infrastructure could also lead to unreliable integration processes, particularly if employees choose to create their own routines besides those described in the system. Value creating processes must act together and there should be aligned and balanced intraorganizational coordination capabilities, in order to achieve a value chain that is wellmanaged [START_REF] Stank | Supply chain collaboration and logistical service performance[END_REF]. Such a well-managed value chain is referred to as an integrated value chain that provides optimized value for the customer [START_REF] Morash | Supply chain integration: customer value through collaborative closeness versus operational excellence[END_REF][START_REF] Stock | Logistics, strategy and structure[END_REF]. As such, focusing on the interfaces between functions or process steps has been relevant for decades. Literature has various interpretations of the term "integration", the content and framing are varying, and few authors present a formal definition [START_REF] Pagell | Understanding the factors that enable and inhibit the integration of operations, purchasing and logistics[END_REF][START_REF] Frankel | Cross-functional integration revisited: Exploring the conceptual elephant[END_REF]. The main purpose of this article is to extend existing knowledge identifying the enablers and disablers of integration within the value chain for different sectors. By studying what enables value chain integration, and which mechanisms are used to facilitate integration in five different organizations within different sectors in Norway, the following research questions will be addressed:

Research question (RQ) 1: What are the enablers of better value chain integration? RQ2: How can firms increase the degree of integration throughout the value chain?

2

Theoretical background: Enablers for achieving integration in value chains.

Much of the existing literature defines integration solely based on the construct of information flow. However, other studies operationalize the concept to include both cooperation and collaboration. On the basis of mainstream operations management research, [START_REF] Pagell | Multiple case studies of team effectiveness in manufacturing organizations[END_REF] formulates the following definition: "Integration is a process of interaction and collaboration in which manufacturing, purchasing and logistics work together in a cooperative manner to arrive at mutually acceptable outcomes for their organization."

This definition is based on the two key components of cross-functional collaboration and interaction. According to [START_REF] Frankel | Cross-functional integration revisited: Exploring the conceptual elephant[END_REF], this definition is "one of the clearest definitions of cross-functional integration". Given then our understanding of the core concept of integration, we carried out a review of the extant literature in order to uncover the key enablers of greater value chain integration, providing an answer to RQ1. The following seven enablers for achieving integration in value chains were formally identified during the literature search, and provide the basis for the rest of the investigation: 3

Research design

The research approach adopted for this study is a multiple case study design that builds on the identification of enablers for value chain integration that were identified in the previous section. Partly to serve as illustrative cases and partly to demonstrate practical usage of the integration theory, the case studies were conducted in different industries and different types of companies. The cases also serve to provide empirical insights into enablers and disablers of better integration in internal value chains. The main reason for choosing a case-study approach, according to [START_REF] Karlsson | Researching operations management[END_REF], is its distinct advantage in situations wherein "how", "what" and "why" type questions are posed in order to understand a complex phenomenon. When selecting cases for studying, there are several criteria to consider, i.e. what data are accessible, type of context and if the data is suitable for testing for the chosen approach. Within this study, the dominant criteria for selecting the case organizations has been the convenience sample [START_REF] Marshall | Sampling for qualitative research[END_REF]. We chose to study the phenomena within different industries in order to have the possibility to illustrate the topic from different perspectives and to build a foundation for the research to be generalizable for different industries. To increase the robustness of the research [START_REF] Patton | Qualitative evaluation and research methods[END_REF], data triangulation was ensured by using multiple sources when collecting the data, such as documents and direct observations in the field [START_REF] Yin | Case study research: design and methods[END_REF].

Case study overview

The case companies in this study are two mass producers (MPI & MPII), a craft producer (CP), a hospital (H) and a service provider (SP), each of which are presented below. The units of analysis in these different organizations are the value-adding elements of their internal value chains. As stated in [START_REF] Pagell | Understanding the factors that enable and inhibit the integration of operations, purchasing and logistics[END_REF], "the only way to truly assess the level of integration is by collecting data from respondents responsible for different value creating processes." Consequently, this research focuses on ensuring that at least two employees were interviewed within each process step of the value-adding elements of the value chain. Interviewees ranged from operators and team leaders to more senior managers, as well as trade union representatives. A summary of the case studies can be found in the following table: Based on the theory from the literature study and the observations made during the case study research, we have been able to construct a model that provides insight into the relationships of each of the enablers for value chain integration. The model is illustrated in Figure 1:

Fig. 1. Theoretical Framework for Value Chain Integration

This model can be used as the basis for a value chain integration maturity assessment tool, to help improve the integration of value chains in and across organizations. To exemplify how this could be applied to improve the integration of a value chain, we provide a theoretical example using data from MPII. The mechanisms were rated according to the extent to which the researchers found evidence for each mechanism during the case study, following the scale defined. (It should be noted that this rating should normally be performed by the company representatives themselves, who would rate the mechanisms according to their own experiences with them). After the rating procedure, the average rating per category is calculated. Table 3 presents The result is a maturity level on the scale 1-5, where 1 indicates a very poor level of value chain integration and 5 indicates very good level of value chain integration. Figure 2 provides an overview of results for case company MPII, that can be used by the firm to identify and prioritize areas for improving value chain integration at the firm:

Conclusion and further research

The results of this research support the view of [START_REF] Turkulainen | Cross-functional integration and performance: what are the real benefits[END_REF] in that integration is a multidimensional concept. This may explain why, when studying an individual category, it is often seen that it can directly or indirectly influence one or more other categories. It can also be observed that some enablers may be placed under several categories and that an enabler could in fact become a disabler, or vice versa, depending on the circumstances. Despite several years of research on the topic of integration, there remains a need for further research to achieve a greater understanding of this concept [START_REF] Frankel | Cross-functional integration revisited: Exploring the conceptual elephant[END_REF][START_REF] Autry | Reconsidering the supply chain integration-Performance relationship: In search of theoretical consistency and clarity[END_REF]. Many different terms and definitions are used within this field, and some authors do not use any definition at all. Given that such inconsistency exists within this area of study, this research was intended to address the need for greater clarification and to provide a holistic overview of integration measures in the value chain. Furthermore, this article contributes to providing an enhanced understanding of which enablers can influence the levels of integration between two or more process steps. As an initial step toward gaining a more generic understanding of the topic, five case companies were studied. Moreover, a value chain integration maturity assessment model was constructed. This can be used to support practitioners when attempting to improve the value chain integration through identifying actions to strengthen such integration. We realize that it is difficult to make concrete generalizations from a small sample of case data. Future work should therefore develop and distribute a survey instrument to gain a greater sample size, thus contributing to more generalizable theory.

  an overview of the distribution and scoring of several examples within the given categories Consensus, Culture, and Facility & Layout:

Fig. 2 .

 2 Fig. 2. Results of Maturity Model

  The overall strategy is transferred down to individual measures x 4 The employees are involved in the process of deriving KPI's (to avoid mistrust) x 3 The overall strategy is well known x 5 The employees support the overall strategy x 4 There is a correlation between management focus and employee focus x 4 All the managers agree upon the business strategy x mechanisms and creation of lateral relations The personell are available at the value chain x 4 The employees have confidence in systems x 4 Degree of acknowledgement of colleagues x 4 Degree of focus on customer among the employees x 4 Degree of openness among the employees x 4 Degree on focus on the entire value chain x 4 The employees have focus on the interrelation of the process steps x 4 There exists an informal culture x 4 The employees have an information sharing mentality x 4 The use of job rotation is consistent when possible throughout the value chain x 4 The employees have knowledge of other departments x 4 The employees have team work experience x 5 The transfer of managers is used to increase the integration x 5 There is a use of cross functional teams x 4 The employees are used to standardized work x

Table 1 .

 1 Enablers of value chain integration

	Enablers of Value Chain Integration	Identified in:
	Culture (social mechanisms and the creation of lateral relations)	[10, 11]
	Management support (vertical integration)	[2, 3, 6, 11-16]
	Consensus	[3]
	Formalization (standardization)	[17, 18]
	Information systems	[19, 20]
	Facility and layout	[9, 21]
	Measurements and rewards	[22, 23]

Table 2 .

 2 Case study overview

		MPI	MPII	H	CP	SP
	Main Product /	Auto	Aluminium	Thrombo-	Leisure	Insurance
	area of study	components	billets	lysis ward	boats	& banking
	No of employees	37	513	265	20	1200
	No of interviews	11	16	15	12	8
	4

Discussion: Towards a Theoretical Framework for Value Chain Integration

Table 3 :

 3 Maturity Model for Value Chain Integration

Acknowledgements

The authors acknowledge the support from the Research Council of Norway through the research program SFI Manufacturing.