extfieldsofstudy: Physics Business

text:
The analysis of human resource quality in improving employees performance: An analysis of external and internal environment

Considering the importance of the elements of Human Resources in the growth and development of the organization because there are talents, creativity, desires and work activities. Therefore, Human Resources or workers must get special attention from the organization so that if the workforce feels comfortable working, the spirit of work will increase, it results in increased performance and the end, the organization’s goals for obtaining results and profits will be achieved. So, the management of Human Resources (HR) in an organization can work efficiently. Education, skills, and leadership play an important role in being able to influence and move employees to achieve organizational goals effectively and efficiently. This study aimed to determine and analyze the influence of the quality of human resources in improving employee performance at Sawerigading Palopo hospital. This research used a quantitative approach with multiple linear regression analysis tools. The results of this study indicated that the quality of human resources has not been able to describe the working conditions of employees.

Introduction
An agency that wants to carry out its duties and functions well will try to change its employees so that they have good quality and performance, making their employees more productive in carrying out their duties and responsibilities. To obtain employees that are in accordance with what is expected, it is necessary to have an employee development strategy.
The quality of human resources is an important inventory for the organization, considering the importance of the elements of Human Resources in the growth and development of the organization because there are talents, creativity, desires and work activities. Therefore, human resources or workers must get special attention from the organization so that if the workforce feels comfortable working then the morale of the work will increase, it results in increased performance and the end, the organization's goals for obtaining results and profits will be achieved. The quality of human resources is concerned with the quality of human resources, abilities, both physical and non-physical abilities [1,2] . The hospital is a service organization, where the products produced are intangible and come from the service provider which in this case is the officer or we call HR. HR is an important element in both production and service delivery. HR is a part of which service organizations create added value and gain competitive advantage. The resources of other tools and infrastructure are possible to replicate and also belong to other hospitals, but not with HR.
Speaking about HR issues is, of course, related to competence. Competence is generally defined as a combination of knowledge, skills and attitudes or attitudes of an employee so that he can carry out his work. Some experts claim that knowledge and skills are hard competencies while attitudes and behaviors are soft competencies.Assessing the quality of human resources must have clear indicators, because this is a very important thing to do, because, the quality of the population is one of the indicators of monitoring the quality of life of residents in a country. In the analysis of this paper, the more emphasized indicator is on several dominant factors in assessing and showing the quality of human resources including among them the level of education, health level, level of income per capita. Health is a condition where a person when examined by experts has no complaints or there are signs of a disease or genitals. Furthermore, education is an attempt made consciously and deliberately to change human behavior both individually and in groups to mature humans through teaching and training [3][4][5]. Thus the purpose of this study was to determine the impact of the quality of human resources in improving employee performance at SawerigadingPalopo hospitals.

Methodology
This research was conducted at Sawerigading Hospital Palopowhis is located in To'Bulung Village, Bara District, Palopo City.The analytical method used was the multiple linear regression method Gitosudarmo (2001), by the following formula: Explanation: Y = Employee Performance X 1 = Education X 2 = Health X 3 = Income e = eror term b 0 , b 1 , b 2 , = Searched Variable

Results
Analysis based on the hypothesis set in the previous chapter, it is suspected that by improving the quality of human resources through the variables of education, health and income have a positive effect on improving employee performance in hospitals. SawerigadingPalopo. To be able to prove this hypothesis, the researchers distributed questionnaires to respondents and the results of the respondents' answers were then processed using multiple linear regression analysis tools through the help of computer applications statistical SPSS 17. The processed SPSS results are presented through the following table 1.  (X2) is -0.209, means that if the income has decreased by one unit, the Employee Performance (Y) will decrease its score by -0.209 units.Furthermore, to find out the relationship magnitude between the improvement of human resources quality through variables namely education, health and income toward the improvement of employee performance in SawerigadingPalopo hospital. It can be seen through the SPSS processed results found in the table 2 Based on the table above, it can be explained that Value R (correlation) is a value that explains the relationor correlation between improving the quality of human resources through education, health and income variables to improve employee performance Sawerigading Palopo Hospital. Where the value of R = 0.772 or 77.2% explained that the correlation or relationship between improving the quality of human resources through education, health and income variables to improve employee performance Sawerigading Palopo hospital was quite significant.Meanwhile, the value of determination is R Square = 0.596 or 59.6%, which means the dependence between improving the quality of human resources through education, health and income variables to improve employee performance in Sawerigading Palopo Hospiyal was 59.6% while 40.4% was influenced by other factors.

The effect of educational variables on improving employee performance in
SawerigadingPalopohospitals.The analysis showed that the regression coefficient of the Education variable (X1) was 0.843, indicating a positive influence between the education variables with an increase in performance meaning that if Education increased by one unit, the Employee Performance (Y) of employees would have increased scores by 0.843 units. The results of the analysis explained that the educational variable is very influential in improving employee performance in SawerigadingPalopo hospitals. the Regional SawerigadingPalopo Hospital means that if the Health variable increases by one unit, the Employee Performance (Y) score will increase by 0.135 units. These results explain that health is quite influential in improving employee performance at SawerigadingPalopo hospitals.

Influence of income variables on improving employee performance in SawerigadingPalopo
hospitals.Through the analysis by the author, the Income variable (X2) regression coefficient of -0.209, shows a positive effect as well, meaning that if the income variable decreases by one unit the Employee Performance (Y) score will decrease by 0.209 units.

Conclusion
Based on the research results described by the researcher in the previous chapter, in this chapter the researcher will formulate some conclusions from the results of this study as follows: 1. There was a significant influence between improving the quality of human resources through education, health and income variables towards improving employee performance in SawerigadingPalopo Hospital. 2. There was a strong relationship between improving the quality of human resources through education, health and income variables towards improving employee performance in SawerigadingPalopo Hospital where the correlation value is R = 0.772 or 77.2% and the determination is r2 = 0.596 or 59.6%.To improve the quality of human resources through the variables of education, health, and income towards improving employee performance in SawerigadingPalopo hospital, the authors conveyed the following suggestions: 1. That is an effort to improve employee performance, it is expected to the leadership of the SawerigadingPalopo Hospital so that the program to improve the quality of human resources through the variables of education, health, and income is consistently carried out consistently and continuously. 2. And finally, the researchers hope for the management of SawerigadingPalopo hospital so that improving the quality of human resources meets the criteria of physical and health quality, quality of education (knowledge and skills), and quality of life (income) as a start of improving employee performance.


Marketing Analysis of Processed Fishery Products using Interactive Media

This study aims to analyze marketing of processed fishery products by using interactive media in Makassar conducted in April June 2019 took place in the city of Makassar. Data was collected through interviews, observations, questionnaires and literature study. The research method in this study is a descriptive analysis will present a general overview of SMEs marketing of processed fish products as a whole by focusing on marketing by using interactive media. Samples are processed fishery products 8 SMEs located in the city of Makassar with criteria using the owners use at least 2 social media and has been operating for less than 5 years. The results showed that social media are the most widely used in the sales activity (75%) is WhatsApp, Facebook and Instagram. Group 2 media (12.5%) only uses WhatsApp and Facebook and group 4 media (12.5%) using WhatsApp, Facebook, Instagram and Line.. The percentage of consumers prefer buying fishery products in a row is a promotion, place, price and product. Keywords— Fishery Products, Promotion Mix, Interactive Media, Digital Marketing

I. INTRODUCTION
Fishery product processing business is one part of the Agroindustry expected strong competitive and able to survive in a relatively long period of time. But in the era of free trade or globalization requires companies fisheries improve productivity and product quality that still exist in the competition. Therefore, in the development of enterprises, especially SMEs required in order to plan or formulate a proper strategy to improve the business management system (Pranowo 2015). Ibrahim et al., (2013), business continuity seen from the economic dimension, one of the economic driver direct business are market opportunities, production and marketing costs. Alma (2013) explains that marketing management is a process to improve the efficiency and effectiveness of marketing activities undertaken by individuals or by companies. The fact that the field show most SMEs in Indonesia, has some of the same problems, one of which is the lack of knowledge about marketing, due to the limited information that can be reached by SMEs on the market. Setiwati (2014) suggested the research results (Effect of E-Marketing In Online Business Using Social Media) that Online Marketing Strategy positive effect on earnings of Micro, Small and Medium Enterprises (SMEs) in Central Java. Siswanto, (2013) describe the presence of social media with all its advantages and proven to provide facilities that are not less interesting than any other media that requires a high cost in use. If optimal, social media can create a brand image (brand image) for SMEs and satisfaction that will have an impact on loyalty. The accessibility and interactivity of the Internet to bring dramatic changes in the paradigm of conventional marketing. Internet changing the way organizations to design, processing, manufacture, market, and deliver the product. Wider scope of competition also requires integration and coordination between the departments of information, marketing, customer service and other departments. (Chandra, Greorius., Fandy Tjiptono. 2012). At present, the world economy, especially developing countries such as Indonesia, are moving towards a digital economy. Technological developments into the first drive these changes. The world organization OECD (Organization for Economic Cooperation and Development) said that digital innovation is claimed to bring countries closer to sustainable prosperity (Kurniawan, 2017). It is an opportunity and an advantage in marketing the processed fishery products. The use of social media right and true will to minimize time and earn more profits. Theresia Pradiani (2017) describe an increase in orders from consumers by sales using social media. However, the use of social media still need to be developed to make innovations in promoting cottage industry results to be more interesting. Thus, through this research is expected to provide an overview of the development of marketing strategies for businesses of processed fishery products so that it can continue to compete in the future.

II. RESEARCH METHODS
This research was conducted for 3 months, from April to July 2019 in the city of Makassar. In this research using descriptive analysis will present a general overview of processed fishery products marketing of micro small and medium enterprises (SMEs) as a whole by focusing on marketing by using interactive media. And to determine consumer response to marketing acceptance of processed fishery products used questionnaire (via google forms). Samples have 8 UMKM with criteria located in the city of Makassar, has been operating less than 5 years and Owner actively using social media at least 2 which include YouTube, Facebook, Whatsapp, Instagram, Line & Twitter.

III. III. RESULTS & DISCUSSION A. Product Marketing Micro Small Medium Processed
Fishery Products in Makassar 1. Product and price Every small and medium micro enterprises processed fishery products in Makassar has many product variants. Among them there are products that must be processed again for consumption and there are direct consumption. There are different variants of foods and beverages such as fish balls, nuggets fish, "otak-otak", bull without thorns, seaweed crackers, crackers squid, fruit syrup seaweed and others. Here are details of the main products and at what price. With the information technology network connected to the global Internet provide opportunities in marketing products or services. Internet and networking features interesting social media applications is one of the promotional tools are better and cheaper, especially in running a business or businesses. In addition to using a mobile phone, some SMEs which are already used more than two social media.

Fig 1. Percentage usage of social media in small and medium micro enterprises processed fishery products in Makassar
The results showed that each manager of SMEs do not use all the social media (6)  The sales volume of the sales expressed in number of sales amount of physical force or the amount of money that must be achieved. In a company marketing objective is to increase the volume of profitable sales to generate revenue within the meaning optimally and increase profits. The sales volume is a measure that indicates the number or size of the amount of goods or services sold. The following graph average sales before and after the use of social media on SMEs processed fishery in Makassar.

Fig 2. Percentage increase in sales in J4 and J8 SMEs.
The results showed an increase only in two SMEs. Of the average sales before and after the use of social media use social media there was an increase of Sales. At J4 and J8 SMEs increased by 37.68% and 35.98%. J4 on SMEs SMEs and MSMEs using three media J8 using four media.
C. Response and consumer acceptance 1. The use of social media on consumer One key to success for all businesses is to get to know its customers more closely. Social media makes it easier recognition process than before. Basically, social media has changed the way consumers interact and how companies market their products. The presence of social media as a channel of communication can help process transactions quickly. Here's an overview of social media are used by consumers in buying processed fishery products in Makassar Basically, social media has changed the way consumers interact and how companies market their products. The presence of social media as a channel of communication can help process transactions quickly.
Here's an overview of social media are used by consumers in buying processed fishery products in the city of Makassar. Research, social media are the most widely used 60% is whatsapp, a social media interaction which most consumers utilize to communicate in order to get a quick response. However some consumers find privacy in buying the product and feel simply by using social media (Facebook and Instagram) as a promotion manager in the transaction. the second largest is Facebook (28%) and the last is Instagram (18%). 2. Response and consumer acceptance of the purchase processed fishery products . Sumarwan also promoted (2014:377) consumer purchasing decisions are decisions that include consumer decisions about what to buy, whether to buy or not, when to buy, where to buy and how to pay for it. It should be considered before making a purchase decision is the desire that has been rounded to buy the product. And Zanjabila (2017) proved that social media marketing significantly influence purchasing decisions.

Fig 3. Percentage of reasons consumers buy products
Fishery The diagram shows that the main reason for the Promotion of 72.80% while among sellers use social media, has good reviews and is promoted by the account reliable. 68.40% choose the place and 67.20% and 60.40% product price for good taste, standardization of products, form an attractive logo and brand famous. In (Erawati, 2015) states that the promotion influential on purchase decisions in accordance with the opinion of Alma (2013: 181) which is the main purpose of the campaign is to inform, attract, and then influence the increase in sales. A promotional activity if done properly can affect consumers about where and how consumers spend their opinions.

IV. CONCLUSION
The marketing activities of micro and small and medium enterprises in fishery products processed in Makassar using interactive media show that not all social media are used in promotional activities. Social media is Whatsapp and Facebook Instagram 33.3% and 29.20% of the most widely used, Line 4. 20% and 0% Youtube and Twitter. The results indicate an increased percentage of sales only on 2 SMEs. The percentage of consumers prefer buying fishery products in a row is a promotion, place, price and product.


Development of knowledge management in integration management systems in order to increase the organisational performance of construction companies

The previous research on integrated management systems has varied. Most of them used a structural equation model (SEM) or consisted of a literature review. These studies have not yet presented a relationship between integration management systems at the practical level in order to improve organisational performance. These studies have not included knowledge management in their implementation. Research on knowledge management is needed in order to minimise the inefficiency of an organisation. The level of maturity of knowledge management in construction companies is only about 50% (at the practiced level). The biggest obstacle to the realisation of an integrated management system is a lack of knowledge management. Knowledge management is a key standard in integrated management and sustainability reporting. This study aims to identify the most important variables needed in the context of the development of knowledge management and integrated management systems through RII analysis, including developing a job description through RAM RACI R (Responsible), A (Accountable), C (Consult), and I (Inform).

Introduction
Globalization means that organizations have to follow management systems that utilize international policy ISO:2015 and OHSAS. In the construction management discussion of the different management systems such as quality management, health and safety management, and environmental management systems, they are not integrated simultaneously. They are still executed independently, whereas, in reality, they engage the same processes throughout the PDCA cycle [1][2][3][4][5][6][7]. These common elements are combined by ISO:2015 through a high-level structure (HLS). This consists of common elements such as top management commitment, documentation, and recording control, notions of policy, goals and target planning, employee training, communication procedures, audits, control Table 1. Maturity level of knowledge management at the construction company in Indonesia [15]. The research questions are: 1) How does knowledge management affect organizational performance in construction companies through integrated management systems? 2) What In complex and large-scale organizations, it is considered that implementing an integrated management system is important [23]. In the organization, humans are the main factor that is most considered when managing an organization, as has been mentioned in a lot of previous research [15].

Construction company
The integration of a new management system demonstrates that IMS can enable companies to benefit from the creation of synergy, that is not only designed to reduce the time and the associated costs, but also to improve efficiency. The complexity occurring in construction companies is not only due to the technological and process uncertainties: it also comes from the increasing influence of the organizational factors [24,25].
The integration of several different critical factors contributes to the successful initiation of knowledge management and the effectiveness of the use of knowledge management systems [26]. Knowledge management is expected to support the realization of innovation. The creation of innovation is one result of the formation of integrated management systems [8]. Simon examined the relationship between integrated management systems and the performance of innovation management [7]. Therefore, IMS is becoming more popular as companies find it more sensible to integrate their MS's rather than managing them individually [5,8,27]. In addition to this, empirical studies on the scope of integration confirm the idea that companies prefer integration rather than disintegration [1,28].

Knowledge management
The focus of knowledge management on organizational performance is increasing [29][30][31]. Open innovation is the key to improving knowledge management. Knowledge management is used to develop innovation and achieve sustainability. In this case, organizational performance plays a role in achieving the status of being a competitive company [32]. Knowledge management is defined as the ability to improve the skills of individuals to allow them to take effective action. Knowledge management is a process of capturing, distributing, and using knowledge effectively [33]. Knowledge management is grounded inability, shaping the performance of the organization, and the associated management processes [34].
There is still a lack of adequate knowledge (tacit and explicit) on how to combine social, environmental and business aspects into the core processes of the organization and how to overcome existing barriers to encourage companies in the pursuit of their goals [3,35]. As a consequence, interdisciplinary knowledge processes need to be investigated in the context of various fields in an integrative manner [36][37][38]. Integration is a very important feature of research and is debatable [32,39]. The integration of knowledge is an important process in organizations [40][41][42]. The novelty of knowledge management in integrated management systems in construction companies has been shown in Table 2. Table 2  (OP), performance evaluation (PE), and improvement (IM). The methodologies used to describe the implementation of knowledge management are Task Action Responsibility (TART), responsibility assignment matrix using coding, accountable, consult and inform (RAM RACI), supply chain (SC), workflow (WF), and Agent-Based Modelling (ABM). Table 2. Novelty of knowledge management in integrated management systems in construction companies. Ref.

Organization performance
Construction companies are a type of complex organization. An organization can be defined as a system of coordinated action among individuals who differ in the dimensions of interest, preference, and knowledge [51,52]. The existence of organizations arises when people interact in order to increase organizational performance [52]. Organizations can also be defined as a knowledge system with and within an organizational structure. Mayo [53] explained that a social group is one of the basic forces which can be used to decide on the organizational operations. Organizational scholars focus on human factors and seek to humanize management [52]. There are theories of organization that is based on the humanmachine relationship perspective [54].
Organizations need several elements to force the potential impact of organizational performance. Organizations have two dimensions, the institutional environment and the technical environment. Organizational performance can also be influenced by aspirationlevel constraints that are based on the performance of profit, the performance of safety and the performance of productivity. The performance of the organization also can be seen from the resources that an organization produces at a given level of organizational output [55].

Methodology
Knowledge management in integrated management systems was examined using workflow (WF) and RAM RACI methodologies to describe best the activities conducted in the chosen construction companies. WF describes the activities as per the variables above. RAM RACI focused on accountability and responsibility, which is important when defining roles and responsibilities across organizations generally and within projects that the organizations deliver. To demonstrate this, we referred to a 2006/2007 attempt to determine accountability and responsibility using a RACI matrix. At the time, the latest (3 rd ) edition of the Project Management Body of Knowledge Guide (PMBoK Guide) provided an example of a responsibility assignment matrix (RAM) using RACI coding -R (Responsible), A (Accountable), C (Consult), and I (Inform) [56] The variables for knowledge management were substantial; 421. These variables then combine with the knowledge management descriptions, such as knowledge holder, knowledge organizer, knowledge user, and knowledge manager to ensure that knowledge management is accurately implemented.
This research will begin with the identification of the clauses/ variables used for knowledge management as the object of this research. The object of research is the implementation management systems of quality, safety, occupational health, and the environment in construction companies. A literature review of the previous studies on knowledge management related to integrated management systems was contained in the clauses. A construct questionnaire was created for knowledge management and validated in relation to construction companies dealing with quality management systems, safety, occupational health, and the environment. The validation clauses were the results of a pilot survey and the associated reliability and validity test.
The respondents were 19 construction company workers. This sample is representative of 8 construction companies that have a high-risk failure/ death rate in Jakarta, Indonesia. The characteristics of the respondents were that 10% had handled more than 30 projects, 40% of the workers had handled 10-30 projects and 50% of the workers had handled fewer than ten projects. 10% of the workers had worked for less than 5 years in the construction company, 45% workers for 5-10 years, and 45% had worked for more than 10 years in the construction company. The results of the survey respondents were analyzed using AHP methods.

Results and discussion
Not all studies will have a hypothesis for testing. For a research study that is an exploration, for a theory that is not significant or small, it is impossible to draw a hypothesis. To achieve the research objectives of this study, we have constructed a research question based on the literature review.

Mean
The formulation of the mean was as follows: (1) We found that µ = 4.46. This means that the respondents agree with all of the variables required in this research.

Validation
Based on the results of the validation of the content and constructs in the research methodology, we found that 10 clauses were valid for knowledge management content. The clauses used for the knowledge management context were scope (SC), normative reference (NM), terms and definition (TD), organisational context (CO), leadership (LS), planning (PL), support (SP), operation (OP), performance evaluation (PE), and improvement (IM).

Results of RII
As we have a valid variable to measure, next, we found the variables that were important in relation to the integration audit. There was also normalization in the matrix and priorities. Some of the variables had the highest weight of X5.2 (leadership commitment when applying unity of purpose in the development of an integrated management system and implementation when done by a service user). These variables had the highest weight (4.06), which is more than X5.10 (the involvement of people activates an organization in the achievement of strategies, policies, processes, and resources), which was 3.57.

Result of RAM RACI
The lack of integrated management system implementation is related to knowledge management, so in this paper, we added the RAM RACI method to support the implementation of integrated management systems. The variables from the RAM RACI method that were found to be valid have been shown in Table 4.  holder is consultative person that have experience in knowing scope of organization. Knowledge user is distributed and inform so they can act based on sope of organization that already signed.
Clause normative reference documentation and activity is prepare by knowledge manager, what normative reference needed in construction company, the legislation of normative reference used in construction company is done by law development, consult to knowledge organizer and inform to knowledge user. Clause terms and definition documentation and activity prepare by knowledge manager and consult with knowledge holder. Terms and definition that used in costruction company is signed by human relation and development, and socialized/informed to knowledge organiser. Clause context of organization should be signed by top management as knowledge manager that already consult with knowledge holder. Context of organization that already signed should be informed to knowledge user.
Clause leadership is must be responsible and supported by knowledge manager. Documentation and activity in clause leadership is signed by human relation and development. Documentation and activity is informed by knowledge organizer and consult to knowledge holder. Documentation and activity in clause planning is set up by knowledge manager and signed by manager planning as knowledge organizer. This planning consult to knowledge holder and inform to knowledge user.
Knowledge user responsible for preparing documentation and activity in clause support and consult with knowledge manager. This documentation and activity is signed by team support as knowledge holder and informed to knowledge organizer. Documentation and activity in clause operational is responsible by knowledge user an signed by engineer as knowledge holder. Documentation and activity in clause performance evaluation is signed by auditee as knowledge manager with consult to knowledge holder. Documantation and activity that already signed informed to knowledge organizer. Knowledge user is responsible to prepare the documentation and activity.
Preparation of documentation and activity in clause improvement is responsible by knowledge user with consult to knowledge holder. Documentation and activity is signed by quality assurance/quality control as knowledge manager and informed to knowledge organizer.

Conclusions
The clauses/variables used for the knowledge management context were scope (SC), normative reference (NM), terms and definition (TD), organisational context (CO), leadership (LS), planning (PL), support (SP), operation (OP), performance evaluation (PE), and improvement (IM). For the clauses, we found that some of the variables had the highest weight of X5.2 (leadership commitment in applying unity of purpose in the development of an integrated management system and implementation when done by the service user) is 4.06, which was more than X5.10 (involvement of people activates an organization in the achievement of strategies, policies, processes and resources), which was 3.57. The clause used for knowledge management in integrated management systems can best be described through RAM RACI coding. For future research we will develop standard operational and procedur based on RAM RACI to develop documentation and activity should be manage by knowledge manager in construction company.


extfieldsofstudy:
Computer Science
Physics

text:
Profit Optimization Through the Application of Theory of Constraints (TOC): A Case Study in Printing Company

Globalization has made industrial competition to become intensified and more stringent. As technology advances, traditional management is deemed insufficient to meet the needs of many companies in order to achieve their goals. The theory of constraints is considered to be a pertinent method to overcome the faced challenges. The theory is aimed to effectively manage constraints that prevent companies from achieving their goals. It is focused on enhancing system capacity constraints and minimizing inventory and operating costs to increase the profit by managing company’s resources efficiently. The purpose of this study is to apply the theory on a case study in a printing company to manage obstacles in an effective way by defining constraints that prevent the company from achieving its targets. Thus, it can increase the company’s profitability. Results show that there are capacity constraints on glue and laminating machine causing the company to solely produce 72.06% of the total demand. It is suggested that if the company adds one more laminating glue machine, an increasing profit as much as 22.33 % can be attained.

Introduction
Limitation of resources to produce products in order to meet market demands is considered to be a constraint that may prevent many companies from achieving their targets. The theory of constraints (TOC) is considered to be a pertinent approach to help companies to improve their performances and efficiency [1].
TOC is very suitable to handle production capacity constraints by identifying bottlenecks and increasing capacity on the identified bottleneck system. Thus, it may benefit companies to increase their profits [2].
According to the theory, company's performance can be measured through three parameters, namely throughput, inventory, and operating costs [3]. Throughput is the rate of product being produced per unit of time. Throughput rate is inversely proportional to the cost associated with raw materials and supplies. When the throughput increases, the cost associated with raw materials and supplies will decrease. This could happen due to the throughput is obtained from the reduction of selling price and materials and supplies cost [2]. Inventory cost is the total of production and labor costs [2]. Whereas operating cost is cost expended to undertake production activities apart from material costs [2].
 The main objective of a company is to make profit: If the company is assumed as a chain, the strongest chain is the weakest child chain. From this point of view, the weakest child chain need be found and strengthened.  Direct labor cost is considered as a part of operating cost: according to the theory, all costs are associated with the operational cost unless the direct materials and inventory costs. Profitability is measured by calculating the selling price, number of sales and direct materials cost and inventory costs. In addition, direct labor costs and manufacturing overhead costs are defined as fixed costs.  There are external and internal factors that prevent the company from making a profit. It is easy to identify capacity constraints because it generally occurs in the production process.  Planning the production flow is a difficult task since constraints/bottlenecks must be defined and changes in products, and product mixes must be considered.
The Theory of Constraint (TOC) has 3 focus parameters namely throughput (outcome), stock (raw material costs), and operating costs [3]. The following is an explanation of these parameters.
 Throughput (Outcome) is found from the results of a reduction between the selling price of the product and the cost of raw materials. Contribution margins are found by reducing all variable costs (direct raw materials and inventory costs, direct labor costs, variable parts of overhead costs) from the selling price. Contribution of margin and throughput are different although seems similar. Louderback & Patterson (1996) says that throughput is found by reducing the cost of direct raw materials and inventory costs from the selling price. Because only variable costs contain direct raw material costs and inventory costs in the TOC (Okutmus et al., 2015). Okutmus et al., (2015) said that output or throughput is a measure found by reducing raw material costs and inventory costs from the selling price of the product (Tiryakigil, 2011)  Stock or Inventory is defined as money invested for producing a product, which is the total of production cost and labor cost [2].  Operating cost is defined as the cost expended regarding the running of business's core operations. Operation costs includes all production costs except the cost associated with raw materials [2]. The application of TOC consists of 5 steps as know 5FS:  Identifying the constraints  Exploiting the constraints effectively  Subordinating every related decision to the constraints  Elevating the constraints  Repeat the process

Company's Information
This research took place in a local printing company, in Jakarta, Indonesia. The company applies 7 hours of work on Monday to Friday, and 5 hours of work for Saturday. The company recently has a bottleneck problem within its production system. It has 5 work centers, i.e. cutting, printing, ponding, varnishing, and laminating. All work centers have the same time capacity every month. During May 2018 there were 576,000 seconds time capacity. Additionally, the total demands on May 2018 were 136,000 unit, and there were 27 types of product had different demand quantities.
The company produces a product, which consists of two parts, namely the top and bottom. The upper part passes through all work stations except the vertical engine. Varnishing machines are used to varnish the bottom part. After the upper part is finished on the glue and laminating machine, the product will return to the ponding machine for finishing the ponding process. Then it will be cleaned and packed with the bottom. While the bottom does not pass through the process in the printing machine and glue & laminating machine.

TOC Running
3.2.1. Constraint Identification. The first step of the TOC is to identify constraints. Identification is done by comparing the theoretical capacity and the actual capacity. Theoretical capacity is the available capacity of active working time. While the actual capacity is the capacity of active work time that is actually needed. If actual capacity exceeds the theoretical capacity, then constraints will occur. The following table shows the comparison between theoretical capacity and actual capacity of each machine. As an example the product A2 works in a cutting station with a capacity of 200 sheets/second or 0.15 seconds/sheet. The request of A2 product is 5,000 units. So that at the cutting work station, the actual capacity is 0.15 x 5,000 = 750 seconds. At A2 print work station requires actual capacity of 1 x 5,000 = 5,000 seconds. The ponding work station requires actual capacity of 4 x 5,000 = 20,000 seconds. Whereas glue & laminating work stations require actual capacity of 12 x 5,000 = 60,000. The calculation is carried out on all products so that the actual capacity required by each work station will be obtained. After that, a comparison is made between the actual capacity and the theoretical capacity. For example, the actual working capacity of glue and laminating stations is 1,032,000 seconds. This exceeds the existing theoretical capacity of 576,000 seconds. So there is a difference of 456,000 seconds. In other words, the glue & laminating machine is jammed. This is called a constraint (bottleneck). The source of these constraints is called capacity constraints.

Exploiting the Constraints Effectively.
At this stage, the throughput rate is calculated by subtracting the raw materials and supplies costs with the selling price of product [2]. Table 2 below shows the throughput rate calculation for each product. Then calculate the constraint rate per product based on the time limit on the work stations which exhibit the bottleneck (glue & laminating). Table 3 shows the constraint rate of the product based on the time constraint. Based on the table above, product B12 have the highest constraint rate. So that the product is prioritized to produce. The next priority sequence is sorted by the value of the constraint rate from the highest to the lowest.
Based on table 3 it can be seen that from 18 types of the bottleneck product, only 12 types can be produced. And A4 products can be produced as many as 500 units of the total 5,000 unit requests. This happens due to the insufficient production time capacity which causes the company fail to meet all customer demands [2]. The next step is calculating the profit based constraints. Table 4 below shows the company's attainable profit. Total selling is obtained by multiplying the selling price with demand. Total raw material cost is obtained by multiplying the raw material cost with demand. Total running expenses are obtained by multiplying running expenses with demand.

Subordinate Every Related Decision to The
Constraints. This research will provide three possible decision alternatives, namely adding one more of both glue & laminating machines, implementing an outsourcing for the remaining products that cannot be done to other companies, and holding overtime hours. In addition, special shifts can also be made. However, it is difficult to do so due to the difficulty of finding labors to work for the additional shift and it would expend very large operational costs.


The Effect of Teaching Innovation on Learning Effectiveness among the Students of Taiwan and Mainland China Universities

This paper presents a teaching innovation that has proved successful in stimulating the in-class participation and learning effectiveness of Asian students. The sample population of the research was the teachers and the students of universities in Taiwan and Mainland China. Convenience sampling was used. Grey Relational Analysis (GRA) was used to explore the feedback of learning course. The research results showed that (1) students in Taiwan and Mainland China both have a good confidence in attendance and teamwork projects at the beginning of the course and; (2) Taiwanese students are confident in their own creativity and application ability at the beginning of the course and students from Mainland China have a regular habit of reading relevant marketing books.

Introduction
Practice teaching is the essential supplement to the traditional theory teaching. Providing new answers to the question of how to teach and learn innovation enables higher education institutions to effectively pursue their task to contribute to social economic renewal. However, not many higher education institutions do pay attention, or succeed well in performing their societal duties, including their roles in industrial, regional and national innovation systems. Hampden-Turner's (2009) study indicated that there are good reasons to be sceptical about teaching innovation and a closely related activity, entrepreneurship. The main challenge is that innovation deviate from what had existed earlier because the objective is to generate novel combinations, which outperform the previous ones. We know surprisingly little about the preparation for practice teaching that occurs in the university context, particularly with respect to these more practiceoriented classes (Clift and Brady, 2005). What we do know has more to do with titles of these courses, or their place in the curriculum, than with how these courses are taught. How to strengthen the practical teaching, and to improve the training quality, is an important and urgent task for the undergraduate teaching in higher institutions. On the other hand, from the point of view of the professional characteristics, marketing is a very practical field of study, graduate students of marketing not only need to have a solid theoretical foundation, but also a strong ability to solve practical problems faced by the enterprises. Therefore, the process of teaching marketing must attach importance to the training of students' operating and management skills, through the establishment of the practice teaching system in order to train students in terms of practical ability.
Overall, the constant renewal of teaching methods and the integration of practice teaching into teaching shall bring a relatively effective foundation between teaching and learning for teachers and students. In addition, students' willingness to learn and their learning effectiveness can be enhanced. This study was attempted to verify and comprehend whether there was an interaction effect of teaching innovation and integrating information technology into teaching as far as students' learning effectiveness in higher education institutions in Taiwan and Mainland China is concerned. Therefore, the specific purpose of the study can be summarised as follows: 1) To verify and comprehend whether teaching innovation has a positive, significant, direct effect on learning effectiveness of higher education institutions in Taiwan and Mainland China. 2) To find out whether the results of this study can be used as a reference when higher education teachers are developing teaching methods.

Learning effectiveness
This literature stems from cognitive psychology and analyses of the constituent elements of learning. These elements located at the higher end of the learning process characterise by one's desire to comprehend and absorb a topic during the years of formal education. Nowadays, there also are more and more challenges for graduate students of marketing, who have to learn more theoretical skills and practical ability to face their future jobs in the industry. These range from the quantitative increase of one's notions and knowledge to learning as an interpretative process aimed at the understanding of reality (e.g. Saljo, 1979), and to changing as a person (e.g. Marton et al., 1993). Furthermore, learning performance will be affected by teaching, course design, learning styles and other factors (Loo, 1999). Jones (1996) shared this view as well. However, the traditional classification of students according to learning attitudes includes "deep" learners, the ideal type where students engage in the understanding of meaning (Biggs, 1987); "surface", learners where students memorise and reproduce information (Biggs, 1987); and "achieving" learners , where students focus predominantly on grades (Entwistle and Ramsden, 1983). Therefore, the conceptual definition of "learning effectiveness" is the ability "to use teaching and learning innovation after graduating, to demonstrate professional skills, and the capability to participate in various external exams, etc. as the indicators of measurement for learning effectiveness". Teachers are thus vitally significant in terms of influencing students' development or redirecting them to deep learning attitudes, particularly when they adopt teaching methods that focus on students' learning rather than content delivery (e.g. Meyers and Jones, 1993;Kember, 2001). Student-centred teaching emphasises the application of "active learning", in which students directly engage in their learning process through learning-by-doing and learning-by-learning. In-class participation is a component of the basic elements of active learning (talking and listening; the remaining are reading, writing and reflecting), which "involve cognitive activities that allow students to clarify, question, consolidate and appropriate new knowledge" (Meyers and Jones, 1993, p. 21). As a result, it contributes to students' development of independent learning skills, knowledge, and other deep learning attitudes that are beneficial to one's career.

Teaching innovation
A general definition of competency is the level of integration of knowledge, skills, and attitudes (Tigelaar et al., 2004). In the education literature, some of teachers' generic competencies is to build teachers' professional development (e.g. Koster et al., 2005;Runco, 2003). There are also many studies to focus on teachers' field competence, research competence, curriculum competence, lifelong learning competence, socialcultural competence, emotional competence, communication competence, information and communication technologies (ICT) competencies and environmental competencies as general teacher competencies (Hannon, 2008;Karwowski et al., 2007;Sahin and Thompson, 2006). However, there is a research gap which investigates the core competencies underlying teachers' innovative teaching. By reviewing related literature and integrating the main findings and results from the previous research, this current research postulates that three core competencies are considered important to underline teachers' innovative teaching: learning competency, educational competency, and social competency (Cairney, 2000;Chen, 2009;Ferrari et al., 2009;Lin, 2009;Pantic and Wubbels, 2010;Robison, 2001;Runco, 2007;Sternberg and Lubart, 1999). To us, this means that teaching and learning innovation practice focuses on the urgency of putting more emphasis on the first mentioned aspects.

Teaching innovation and learning effectiveness
When it comes to teaching innovation, according to Bruce (1989), "»learning« occurs in the interaction between the »learner« and the »learning environment«, when the appropriate strategies and methods were applied to use technology making it a favourable tool for teaching, then a better teaching effectiveness can be achieved". Teaching innovation means the teachers having creativity, being able to reflect on, to design and to apply new, diverse teaching methods or activities, understanding individual differences of students, stimulating students learning motivation and interest, enhancing the students learning effectiveness in the preparation before teaching, in the process of teaching and in student assessment (Chen, 2010). In short, teaching innovation is teachers having creativity and showing vivid and lively teaching strategies to make students interested in learning, thus enhancing the teaching effectiveness of teachers. In this article, it is suggested that a practice-theoretical approach provides a good basis for teaching and learning innovation in higher education and marketing courses. In this case study, it is explored how a practice-oriented model of teaching and learning has been injected into the education study curriculum of Taiwan and Mainland China higher education institutions.

Methodology
According to the criteria of Daniel's extraction for critical success factors, this study applies grey relational analysis (GRA) to analysing the differences of learning factors for Taiwanese and Mainland Chinese students, to exploring the feedback on learning marketing course. The result expects to improve teaching effect during this process. Furthermore, Chung Hsing University in Taiwan has obtained funding as part of the Ministry of Education's program -the Aim for the Top University Plan and Sun Yat-sen University in Mainland China is one of the universities sponsored by the key Project 985 which provided funding to the University. Both these institutions, were selected as the representative samples of both countries. Finally, marketing course was selected in this study, as both (China and Taiwan) are the same East Chinese societies, which can reduce the data differences caused by different teaching methods.

Questionnaire design and recycle
There are 26 factors in the questionnaire (see Table 1), divided into five dimensions in this study, including "Learning intention", "Practical skills", "Teamwork", "Professional knowledge" and "Creativity". This study uses the GRA of all the factors without subdividing them into each category as there were few factors in each category. Participants are required to choose the agreement level of each factors based on their own learning effect assessment, including the time point of the beginning and the end of the semester. This paper not only investigates the fact that students have some viewpoint changes about course learning after a semester, but also compares differences of self-learning outcomes between Taiwanese and Mainland Chinese students. The total repeated questionnaire of Taiwan and Mainland China are 24 and 49 at the beginning of the semester, and 28 and 46, respectively, at the end of the term as well. (1961) has mentioned that there are three to six "critical factors" for success. Also, critical factors cannot exceed half of the total number of factors; otherwise, they will lose their representative aspect. Such methods of analysing success factors can be applied in enterprises, questionnaires and so on. The GRA ranking at the beginning and end of Taiwan and Mainland China marketing course is showed in Table 1. Based on the GRA results, the rankings of factors before and after marketing course in Taiwan and Mainland China are shown in Figures 1-4. The study of Daniel (1961) is taken as the basis for selecting critical factors. For the above reasons and the reference of factor code provided in Table 1, each group of critical factors is shown as followed:

Daniel
Five critical factors are selected at the beginning of Taiwan marketing course and they are: A24 (0.8716) as the top critical factor, followed by A20 (0.8439), A4 (0.8264), A14 (0.7817) and A12 (0.7639). Furthermore, five critical factors are selected at the end of Taiwan marketing course. These are the following: A4 (0.8150) in the first group, A12 (0.7857) and A14 (0.7701) in the second group, A13 (0.7211) and A17 (0.7202) in the third group.
On Source: Authors' own study.
Source: Authors' own study.
Source: Authors' own study.
Source: Authors' own study.

Comparison of students' viewpoints on learning outcomes in both countries before and after the marketing course
The purpose of this study is to explore how to modify the teaching methods in order to improve the learning effectiveness of students in both countries. In addition, in this section, the comparison of learning effectiveness and teaching innovation in both countries is examined. Therefore, this study has analysed the learning effectiveness of Taiwanese and Mainland Chinese students between the beginning and the end of learning marketing course, respectively, in order to compare the viewpoints and differences in terms of learning outcomes between these two groups. Moreover, this study has compared and examined the differences of both groups' learning effectiveness and teaching innovation, respectively.

Critical factors of comparison between Taiwan and
Mainland China before the course This section regards the comparison of two groups. Table 3 shows the comparison of top ten ranking of the GRA results between Taiwan and Mainland China before the course. In this study, there were found three key factors: 1) The most critical factors for the study were A4, A12, and A14 for Taiwanese or Mainland Chinese students. That is to say, students in both countries have confidence in attendance and teamwork at the beginning of the course.
2) The overall ranking of factors in the "teamwork" dimension is significantly higher than that of other categories for both groups of students at the beginning of the course.
3) The differences of both countries at the beginning of the course can be learned from Table 3. Taiwanese students demonstrate creativity and the ability to criticise practice, while Mainland students think they have the basic knowledge of this subject and the habit of reading the relevant books.

Critical factors of comparison between Taiwan and Mainland China after the course
There are three critical factors (see Table 4) to illustrate the comparison of factors after the course between two groups as followed by: 1) A4, A12 and A14 are the same critical factors of learning effectiveness in Taiwan and Mainland China marketing course. 2) It can be found that at the end of the course, the first six categories of factors are the same for both groups of students. More specifically, only A4 is part of the "learning intention" dimension and other five factors are part of the "teamwork" dimension. 3) Taiwanese students demonstrate interest and ability to adapt in marketing after the course, whereas the students from Mainland China demonstrate improvement on practical skills. A24* A20* A4* A14* A12* A13 A15 A17 A16 A1 Mainland China (beginning) A4* A12* A17* A3* A14* A16 A15 A11 A13 A18 Source: Authors' own study. Note: The difference in rank is based on the factor code "Taiwan (beginning)", which is compared with the code in "Mainland China (beginning)".

* critical factor
Source: Authors' own study. Note: The difference in rank is based on the factor code "Taiwan (end)", which is compared with the code in "Mainland China (end)".

Critical factors of comparison before and after the marketing course in Taiwan
After understanding the comparison of the two country's students, this section shows Taiwanese students' learning results in terms of using teaching innovation. Table 5 illustrates the top ten ranking of the GRA results of the comparison before and after the marketing course in Taiwan. The results have shown that: 1) A4, A12 and A14 are all the critical factors at the beginning and the end of the course. It is clearly known that Taiwanese students have recognised the learning effectiveness with regard to teamwork of this course. The confidence in maintaining attendance (A4) has also shown the interest in this courses among Taiwanese students. 2) A20 and A24 are both the critical factors at the beginning of the course, while having a significant decrease of GRA ranking at the end of course. A20 and A24 have declined after the course as Taiwanese students do no thave the slightest idea about practice marketing in real world when compared to marketing knowledge in classes.

Critical factors of comparison before and after the marketing course in Mainland China
This section explains the factors before and after the course in Mainland China: 1) A4, A12 and A14 are all critical factors regardless the beginning and the end of the course, which means that students from Mainland China are confident in maintaining attendance and teamwork of this course. 2) Students from Mainland China are more confident in "teamwork" at the beginning of the marketing course. Table 1, it can be found that A8 and A9 have a significant increase in the ranking. It can be clearly shown that the Mainland students demonstrate increasing confidence in "practical skills" after a semester of this course. 4) It is worth noticing that the ranking of A3 drops from the original 4 to the last. That is due to the fact that students are busy with studying in the semester.

Conclusions
According to the results of this study, students in Taiwan and Mainland China have confidence in attendance and teamwork projects at the beginning of the marketing course. It is obvious that high attendance results from students' interest in this course and the fact that they expect to achieve knowledge about the subject at the beginning of the semester. Secondly, the "teamwork" dimension regards to the fact that students from both groups have tolerance for different opinions. Taiwanese students are confident about their own creativity and the ability of application at the beginning of the course and students from Mainland China have a regular habit of reading relevant marketing books. However, these critical factors at the beginning of the course are all replaced by the "teamwork" dimension at the end of the course. Compared with the ranking of other factors, it is found that Taiwanese students still have a certain degree of confidence about resilience, and "the interest in this course" has improved after the semester. On the other hand, students in Mainland China demonstrate improvement on their "practical skills" after this course. To sum up, Taiwanese students pay more attention to "creativity" and "the combination of interest and learning". In contrast, students in Mainland China are more concerned about "practical skills" and "accumulated learning effect step by step" (shown as A3) when compared to the Taiwanese students. Based on the above it can be concluded that educators should try to implement diversified teaching strategies and methods. Firstly, educators could arrange more group discussions during the course to stimulate students to expand their knowledge. Secondly, teachers could demonstrate a greater flexibility in arranging courses and give students the opportunity to use their creativity. Finally, educators could pay more attention to the combination of theory and practice, while arranging their curriculum, giving students more practical opportunities. When analysing and comparing these two cases, the authors of the article concentrated more on the learning process when teaching innovation is performed.
Looking specifically at professions that share similar challenges in terms of teaching innovation implementation, enables us to see how other educators have responded to the challenges of preparing novices to create relationships with students that are crucial to the success of teaching practice. Therefore, further research should be focused on the specific learning processes, which occur in the classroom and concern students, teachers and other co-operators, during the implementation of innovation practice.


Spatial Monitoring of Wafer Map Defect Data Based on 2D Wavelet Spectrum Analysis

Featured Application: Image based manufacturing process diagnosis and clinical image data analysis for malignancy detection. Abstract: Since machine vision systems (MVS) lead to a wide usage of monitoring systems for industrial applications, the research on the statistical process control (SPC) of image data has been promoted as an automated method for early detection and prevention of unusual conditions in manufacturing processes. In this paper, we propose a non-parametric SPC approach based on the 2D wavelet spectrum (WS-SPC) to extract the feature that contains the spatial and directional information of each subspace in an image. Using the 2D discrete wavelet transform and spectrum analysis, the representative statistic, the Hurst index, is calculated, and a single matrix space that consists of estimated statistics is reconstructed into a spatial control area for SPC. When a control limit is determined by the density of statistics, real-time monitoring based on WS-SPC is available for time releasing images. In the application, an analysis of wafer bin maps (WBMs) is conducted at a semiconductor company in Korea in order to evaluate the performance of the suggested approach. The results show that the proposed method is e ﬀ ective in terms of its fast computation speed and spectral monitoring.

Introduction
Statistical process control (SPC) is one of the monitoring schemes that can be applied to fault detection in manufacturing processes. The basic SPC procedure consists of two phases: the calculation of the statistics to represent the objects' status from multivariate variables and the determination of the control limits that define an abnormal status of the objects.
There has been a variety of research with respect to the monitoring of manufacturing processes. Borionetti [1] introduced multiple indices for process monitoring such as the surface parameters, bulk, and epitaxial layer, which were applicable to the monitoring of the process quality of wafers. Besides, a model for multistage processes from binary data was proposed by Shang et al. [2] by using the hierarchical likelihood approach to illustrate the cumulative effects. Nguyen et al. [3] suggested a three-stage prognosis for batch manufacturing composed of the extraction of the health index, the variation of representative values using the percentile measure, and the modeling of profiles as gamma processes.
As an advanced process monitoring methodology, the machine vision system (MVS) is actively used in the manufacturing area. MVS is an automated two-dimensional inspection system that monitors whether the objects of interest (e.g., products, processes) are in the normal status or not.
This system has an advantage over manual investigation in that the inspection can be performed with less time and at a lower cost. Currently, the need for further research on automated two-dimensional (2D) monitoring algorithms is becoming greater to reduce the high inspection costs. MVS is repeatedly operated in the given sequence; obtaining images, analyzing raw data using an appropriate model, and training and updating the constructed model. In the general manufacturing area, MVS is applied to SPC to maintain productivity, diagnose manufacturing processes in real time, and detect faults early [4]. For instance, MVS is utilized in the quality control of wafers, which are the basis of electronic integrated circuits (ICs) as components of semiconductors. As shown in Figure 1, silicon crystal, called ingot, is melted by heat and sliced after hardening. The wafer is then fabricated in the manufacturing process through a series of masking, etching, and polishing to generate the IC. In the process, MVS can be introduced to each processing lot in order to monitor the status of wafers or detect any defective region, which leads to the increase in the yield of the manufacturing process. Though image based multivariate SPC approaches are widely applied to distinguish if the process will have a defective status or not, using a representative value, there are few literature works providing the spatial information of faulty regions; e.g., the defect clustering area in the wafer bin map (WBM) resulting from the semiconductor manufacturing process. Tong et al. [6] applied the traditional Hotelling's T 2 chart to WBM data based on the number of defects and defect clustering indices. However, they failed to provide the spatial information of the clustered defects in WBM.
Lin [7] used a wavelet transform based multivariate process approach for an automated visual inspection of ripple defects. Therein, he showed that wavelet-based methods had better performance than traditional approaches, which use raw images directly. Liu and Macgregor [8] proposed the wavelet texture based T 2 control chart, where principal component analysis (PCA) was added in order to extract the feature vector in an efficient manner. To provide spatial information on image data, Tunák et al. [9] introduced the spatial chart to illustrate the density of woven images based on 2D discrete Fourier transform (DFT), which localized wavelet coefficients in the frequency domain. Later, by segmenting a single image matrix into 1D signals, Koosha et al. [10] extracted wavelet basis functions from textile image data to detect out-of-control conditions using a generalized likelihood ratio chart, which was proposed by Reynolds and Liu [11]. Existing approaches employ signal processing methods in SPC to improve the detection accuracy of abnormal status from (spatial) image data. The majority of studies assume normality or a pre-specified distribution a priori to calculate the statistics designating the control limits of a control chart. In reality, the process variables from (spatial) image data hardly follow a normal distribution. Moreover, almost entirely abnormal (or defective) regions in an image present relatively irregular patterns for each faulty area.
In this research, we propose a wavelet spectrum based 2D SPC (WS-2DSPC) to provide a robust output by considering the energy of wavelets, mainly under a nonparametric framework. A hierarchical structure decomposed by 2D discrete wavelet transform (DWT), which was proposed by Nicolis et al. [12], is introduced to obtain the directional and spectral information of the faulty area. Then, the Hurst index as the representative statistic is estimated to reflect the nonstationary property of wavelet coefficients in fractional Gaussian fields. Finally, WS-2DSPC is constructed by setting the control limits based on the density of Hurst indices from image windows. The proposed method is applied to real WBM image data to prove its potential in fault detection or process monitoring.
The rest of this study is organized as follows. In Section 2, we briefly review 2D DWT and wavelet spectrum analysis. The conceptual procedure of the proposed idea is presented in Section 3. Though image based multivariate SPC approaches are widely applied to distinguish if the process will have a defective status or not, using a representative value, there are few literature works providing the spatial information of faulty regions; e.g., the defect clustering area in the wafer bin map (WBM) resulting from the semiconductor manufacturing process. Tong et al. [6] applied the traditional Hotelling's T 2 chart to WBM data based on the number of defects and defect clustering indices. However, they failed to provide the spatial information of the clustered defects in WBM.
Lin [7] used a wavelet transform based multivariate process approach for an automated visual inspection of ripple defects. Therein, he showed that wavelet-based methods had better performance than traditional approaches, which use raw images directly. Liu and Macgregor [8] proposed the wavelet texture based T 2 control chart, where principal component analysis (PCA) was added in order to extract the feature vector in an efficient manner. To provide spatial information on image data, Tunák et al. [9] introduced the spatial X chart to illustrate the density of woven images based on 2D discrete Fourier transform (DFT), which localized wavelet coefficients in the frequency domain. Later, by segmenting a single image matrix into 1D signals, Koosha et al. [10] extracted wavelet basis functions from textile image data to detect out-of-control conditions using a generalized likelihood ratio chart, which was proposed by Reynolds and Liu [11]. Existing approaches employ signal processing methods in SPC to improve the detection accuracy of abnormal status from (spatial) image data. The majority of studies assume normality or a pre-specified distribution a priori to calculate the statistics designating the control limits of a control chart. In reality, the process variables from (spatial) image data hardly follow a normal distribution. Moreover, almost entirely abnormal (or defective) regions in an image present relatively irregular patterns for each faulty area.
In this research, we propose a wavelet spectrum based 2D SPC (WS-2DSPC) to provide a robust output by considering the energy of wavelets, mainly under a nonparametric framework. A hierarchical structure decomposed by 2D discrete wavelet transform (DWT), which was proposed by Nicolis et al. [12], is introduced to obtain the directional and spectral information of the faulty area. Then, the Hurst index as the representative statistic is estimated to reflect the nonstationary property of wavelet coefficients in fractional Gaussian fields. Finally, WS-2DSPC is constructed by setting the control limits based on the density of Hurst indices from image windows. The proposed method is applied to real WBM image data to prove its potential in fault detection or process monitoring. The rest of this study is organized as follows. In Section 2, we briefly review 2D DWT and wavelet spectrum analysis. The conceptual procedure of the proposed idea is presented in Section 3. Section 4 provides an application of the proposed idea to WBM image data for SPC purposes. This study is concluded in Section 5, along with possible future research.

2D Discrete Wavelet Transform
2D discrete wavelet transform (DWT) is a transform method for constructing blocks with discretely sampled wavelets. 2D DWT is well known as a powerful tool for real-time image analysis due to its computational efficiency. It converts the data of a single image into multi-resolution sub-band areas, which help self-similarity analysis. The decomposed output contains a set of basis functions called wavelets, which can be localized in the time and frequency domains and reconstructed through inverse DWT. The converted function consists of translations and dilations of univariate scale function φ j,k (t) = 2 2 j φ 2 j t − k and wavelet functions ψ i j,k (t) = 2 2 j ψ i 2 j t − k , for two-dimensional timelines t = (t 1 , t 2 ) ∈ R and location factors k = (k 1 , k 2 ) ∈ Z. Here, i has three directions: diagonal (HH; High-High sub band), horizontal (HL; High-Low sub band), vertical (LH; Low-High sub band), and j denotes the resolution level. Based on scale and wavelet functions, the two-dimensional image is transformed as: where L and J are the lowest number of the resolution level and the highest number of the resolution level, respectively. Through the hierarchical calculation, the coarser coefficients c j,k (also called approximation coefficients) and the detailed coefficients d j,k can be obtained. Each of them is defined as: Approximation coefficients capture the smooth and general information in the low frequency area, while the detailed coefficients characterize the detailed and oscillatory pattern in the high frequency region. Detail coefficients provide the vibrating behaviors of original image signals; thus, all the de-noising and feature extraction techniques are conducted based on the detailed coefficients.

Spectrum Analysis
As mentioned before, wavelets at each resolution level are an effective tool for detecting self-similarity. 2D fractional Brownian (fBm) motion is a representative model to explain a self-similar continuous-time Gaussian process in two dimensions. For self-similar images to be non-stationary with zero mean, let us define a scaling function as B H (u) and B H (v), where u and v are the row position and the column position of the selected area, respectively. An autocorrelation function between the two scale functions is derived by a combination of the Euclidean norm · as: where H ∈ [0, 1] denotes the Hurst exponent, which is a measure of the long range dependence of time series data. Here, Γ(·) denotes the gamma function. The application of 2D fBm enables the increment of images to be stationary. The formula with respect to the increment h is given as: Equation (4) shows that the oscillating patterns of B H (u) are dependent on the distance of increments h . Derived from this relationship, the power spectrum of scaling functions can be simplified as [13]: Based on Equation (5), we can estimate the appropriate Hurst exponent from the given image. According to the values of H, the original signal images can be characterized; for example, with the Hurst exponent close to 0.5, the image has a random property, without any correlation or trend; whereas a large H value means that the image is manifested differently, indicating a long range persistent time series, while a small H value represents anti-persistent time series having a short length of the mean reverting property.

Wavelet Spectrum Based 2D Statistical Process Control
In this section, we propose a wavelet spectrum based two-dimensional statistical process control (WS-2DSPC). WS-2DSPC is a procedure to detect localized faulty regions, as well as overall defective regions without any distributional assumption. First, image features are selected from 2D DWT, and wavelet spectrum analysis is performed by calculating the fault strengths of divided windows. In order to obtain the spatial characteristics from a single image, the image should be divided into multiple windows to apply 2D DWT to each window. After dividing the image, 2D DWT is conducted for each window in order to capture the potential variability of signals, where the hierarchical structure of the detailed coefficients can be constructed, where the energy of the detailed coefficients is calculated for each resolution level. Once energies are calculated for each resolution level, a linear regression model can be constructed so that Hurst exponents of the divided windows are estimated as representative values. Based on the indices, a matrix of control statistics and control limits is computed, then the spatial control chart for the image is constructed. A detailed description of the WS-2DSPC procedure is listed below.

•
Step 1: Remove noise regions from raw images by applying the universal thresholding suggested by Donoho and Johnstone [14], and transform the images into gray-scale. Thresholding is a de-noising method to remove the converted wavelet coefficients smaller than a specific threshold value. This step could be omitted if the images do not include any noise. In this step, we decomposed the applied images into the maximum resolution level to minimize the loss of useful information and preserve the local defects as much as possible.

•
Step 2: Split a d × d matrix of image X into multiple sub-matrices as windows, W 1 , W 2 , . . . , W n×n , which are given as: with the total number of windows n 2 . Each window is divided into a d n × d n square matrix. To apply 2D DWT, it is recommended that both the image and divided windows have a 2 l × 2 l size format for a positive integer l. The larger width and height the windows have, the more possible is the precision of the monitoring; thus, the trade-off between accuracy and computation time must be considered. In order to obtain sufficient information from the wavelet hierarchy, it is desirable for the height and width of windows to be more than 2 5 .

•
Step 3: Conduct window-wise 2D DWT to extract the representations of divided images generated by wavelets. The number of resolution levels is dependent on the dimension of the sub-matrices. If the size of windows is 2 l , 2D DWT provides l resolution levels containing wavelet coefficients. From this, three wavelet matrices per each window are generated, where the results come from three directions: horizontal, vertical, and diagonal. Conducting 2D DWT for every window using Equation (1), the l th window matrix is decomposed as: • Step 4: Estimate the Hurst exponents of each window in three directions using wavelet spectrum analysis, which is used for calculating the control statistics of WS-SPC. As mentioned in Section 2.1, the detailed coefficients actually work as the key for capturing the vibrating pattern and energy of objects. The wavelet function of the original image (2) can be re-written with a self-similar model in the fractional Brownian field, B H i l (u), and the detailed coefficients of a single window d i,l j,k can be defined as: where the detailed coefficients have zero mean and variance: If there is some replacement p = 2 j (u − v), q = 2 j v − k for a simplification, Equation (8) is modified as: Taking the natural logarithm of the variance of Equation (9), we can obtain the regression form of wavelet energy: where C i l denotes a constant composed of the integrals of wavelet functions. Using the generalized power spectrum and linear relationship, the Hurst exponents and constants of the windows are estimated.

•
Step 5: Define the control statistics and control limits using the density of statistics to monitor the image process. In order to distinguish the irregular pattern from a normal region, the distance between each Hurst exponent and mode of windows is calculated. Since there are three features generated from each direction of 2D DWT, the distance matrix (denoted as D ) for all features to the background are considered as: Appl. Sci. 2019, 9, 5518 6 of 10 where Bg H i is the background values or matrix of features and H i is the Hurst matrix of a whole image containing individual (scalar) Hurst exponents from each window in direction i as: For the distance measurements, the L1 norm is selected due to its appropriacy for the sparse model, where the distance matrix with a large normal region should be treated as the sparse matrix. From the above equation, the distance matrix D is generated as the features of control statistics, which represent the degree of faults and energies with spectral information. Then, the control limits for the distance matrix are determined by the distribution of D. In addition to the spectral information, we can calculate the control limit by estimating the kernel density of {dist 1 , dist 2 . . . , dist n×n }. Density estimation results show that the normal region N and suspicious regions S are divided into two parts based on the points of the valley in the distribution. If a probability of a suspicious region is more than the pre-determined rates, the maximizer of the suspicious density, argmax dist∈S f (dist), is considered as the upper control limit (UCL).
In WS-2DSPC, pre-processing of the image and division of a single image into multiple windows is conducted. After wavelet coefficients are obtained by 2D DWT, the Hurst matrices from three directions are generated from spectrum analysis, where the distance matrix is calculated from the Hurst features to represent the status of processes as control statistics. Hereafter, the calculation of the control limit is conducted based on the density of the distance matrices, which enables us to detect the abnormal status containing a high amount of energy distance from the original background. The suggested method gives robust results in a nonparametric framework with computational efficiency, regardless of the hyperparameters or pre-specified distribution. Using this procedure, a spatial monitoring scheme for 2D image data, as well as the calculation of fault strengths in the divided region can be provided.

Application and Discussion
In this section, we applied the suggested algorithm to wafer bin map (WBM) data provided by a semiconductor company in Korea. This data contained the defective information of WBMs during the manufacturing processes, which provided the X-Y coordinate of defects in each WBM. As shown in Table 1, the identification code of the lot, timestamp, and relative position of defects from the center (0, 0) could be obtained from the data. In order to monitor the continuous status in a single lot, the data generated from a specified lot were considered. Since the suggested procedure aims to control and detect the spatial defects of time series processing based on image analysis, we selected four main types of defective WBM images, as shown in Figure 2. These images consisted of a faulty condition caused by multiple patterns of local defects including scratch, circle, and semicircle (half). Each of these images was separately applied to each step in the suggested algorithm to illustrate the effectiveness of the proposed procedure clearly.  10:00:50 3 −5.369e+00 0.012e+02 … … … … … Since the suggested procedure aims to control and detect the spatial defects of time series processing based on image analysis, we selected four main types of defective WBM images, as shown in Figure 2. These images consisted of a faulty condition caused by multiple patterns of local defects including scratch, circle, and semicircle (half). Each of these images was separately applied to each step in the suggested algorithm to illustrate the effectiveness of the proposed procedure clearly. Proceeding with the proposed method, the original data were transformed into image-type format, where the locations with faults were represented as colored points. Every image had a size of 2 × 2 for 2D DWT, and the size of windows was fixed to 2 × 2 . With respect to the 2D DWT function, "la8" wavelet, the Daubechies least asymmetry length eight, was used. Since random defects in the overall area can be produced during semiconductor manufacturing, global defects were eliminated for effective quantification of faults using universal thresholding with the maximum decomposition level. In addition to noise elimination, the image should be made gray-scale to integrate WBM in one layer. The result of image construction and pre-processing is given in Figure  3, where the scattered points indicating global defects are minimized. Proceeding with the proposed method, the original data were transformed into image-type format, where the locations with faults were represented as colored points. Every image had a size of 2 10 × 2 10 for 2D DWT, and the size of windows was fixed to 2 5 × 2 5 . With respect to the 2D DWT function, "la8" wavelet, the Daubechies least asymmetry length eight, was used. Since random defects in the overall area can be produced during semiconductor manufacturing, global defects were eliminated for effective quantification of faults using universal thresholding with the maximum decomposition level. In addition to noise elimination, the image should be made gray-scale to integrate WBM in one layer. The result of image construction and pre-processing is given in Figure 3, where the scattered points indicating global defects are minimized. After the pre-processing step, the image was divided into square-sized sub-images as windows. As mentioned in Step 3 in Section 3, the hierarchy of wavelet coefficients was obtained by 2D DWT per each window. In Step 4, the energy of the detailed coefficients for resolution levels was calculated so that the linear regression based on wavelet spectrum analysis could be conducted. Figure 4 presents the energy profiles of all windows derived from WBMs, where a single line describes the trend of single windows and the color of lines (red, blue, and black) represents the direction of wavelet transforms: diagonal (HH), horizontal (HL), and vertical (LH) coefficients, respectively. From the comparison of WBMs between the normal condition and abnormal condition with a circle defect, we can observe that the WBM with more faults had greater variation of energies, where the Hurst exponents (slopes of the linear equation derived from each window) were more distributed than the normal status.   After the pre-processing step, the image was divided into square-sized sub-images as windows. As mentioned in Step 3 in Section 3, the hierarchy of wavelet coefficients was obtained by 2D DWT per each window. In Step 4, the energy of the detailed coefficients for resolution levels was calculated so that the linear regression based on wavelet spectrum analysis could be conducted. Figure 4 presents the energy profiles of all windows derived from WBMs, where a single line describes the trend of single windows and the color of lines (red, blue, and black) represents the direction of wavelet transforms: diagonal (HH), horizontal (HL), and vertical (LH) coefficients, respectively. From the comparison of WBMs between the normal condition and abnormal condition with a circle defect, we can observe that the WBM with more faults had greater variation of energies, where the Hurst exponents (slopes of the linear equation derived from each window) were more distributed than the normal status.
presents the energy profiles of all windows derived from WBMs, where a single line describes the trend of single windows and the color of lines (red, blue, and black) represents the direction of wavelet transforms: diagonal (HH), horizontal (HL), and vertical (LH) coefficients, respectively. From the comparison of WBMs between the normal condition and abnormal condition with a circle defect, we can observe that the WBM with more faults had greater variation of energies, where the Hurst exponents (slopes of the linear equation derived from each window) were more distributed than the normal status. Using the Hurst exponents from every window, a distance matrix was calculated from Step 4 in Section 3, where the derivation of distances from the background enabled us to quantify the strength of faults. Based on the matrix, the density of the distance elements, for = 1,2, … , , was estimated. The distribution was split into normal and suspicious parts by the valley, that is the defective region belonged to the distribution that had a greater distance from the local minimal value, because the suspicious area had a further distance from the background than the other. The probability of faults was calculated by the cumulative distribution function (CDF), which was more than the valley. If the probability was more than the pre-specified standard for fault detection, the wafer was determined to have failed.
The visualization of the distance matrix and density estimation from the given images having circle and horizontal defects and half defects is shown in Figure 5. If the degree of defects in a specific window was serious, the window had a greater distance from the background. The kernel density function was separated by each valley, which lied at 0.5564 and 0.6219 in the WBM with circle and horizontal defects and half defects, respectively. The probabilities of failure from two images were estimated as 0.2928 and 0.3394. Since we set the threshold for failure to 0.3 in this application, the half defective WBM would be classified as the faulty state, and the UCL for the spectral control chart was defined as the maximizer of a suspicious distribution. In this example, UCL was set to 1.7461 to detect the failure parts exceeding the control limits. Using the Hurst exponents from every window, a distance matrix D was calculated from Step 4 in Section 3, where the derivation of distances from the background enabled us to quantify the strength of faults. Based on the matrix, the density of the distance elements, dist l for l = 1, 2, . . . , n 2 , was estimated. The distribution was split into normal and suspicious parts by the valley, that is the defective region belonged to the distribution that had a greater distance from the local minimal value, because the suspicious area had a further distance from the background than the other. The probability of faults was calculated by the cumulative distribution function (CDF), which was more than the valley. If the probability was more than the pre-specified standard for fault detection, the wafer was determined to have failed.
The visualization of the distance matrix and density estimation from the given images having circle and vertical defects and half defects is shown in Figure 5. If the degree of defects in a specific window was serious, the window had a greater distance from the background. The kernel density function was separated by each valley, which lied at 0.5564 and 0.6219 in the WBM with circle and vertical defects and half defects, respectively. The probabilities of failure from two images were estimated as 0.2928 and 0.3394. Since we set the threshold for failure to 0.3 in this application, the half defective WBM would be classified as the faulty state, and the UCL for the spectral control chart was defined as the maximizer of a suspicious distribution. In this example, UCL was set to 1.7461 to detect the failure parts exceeding the control limits. Based on the matrix composed of the statistics and upper control limit, a spectrum-based 3D spatial control chart was constructed. The spatial SPC implemented from the WBM image given in Figure 5 is provided in Figure 6, where three axes, X, Y, and Z, stand for the column, the row index of windows, and the control statistics. Because the defective region had a greater distance from the (c) density estimation of the distance matrix of (a); (d) half defective WBM; (e) spectral reconstruction of the distance matrix of (d); (f) density estimation of the distance matrix of (d).
Based on the matrix composed of the statistics and upper control limit, a spectrum-based 3D spatial control chart was constructed. The spatial SPC implemented from the WBM image given in Figure 5 is provided in Figure 6, where three axes, X, Y, and Z, stand for the column, the row index of windows, and the control statistics. Because the defective region had a greater distance from the background, it would have higher values of the control statistics, and the statistics exceeding UCL were detected as faulty regions. The plane for UCL could be generated to detect the faulty location easily with higher values of the control statistics, and the plane was parallel to the XY-plane. (d) (e) (f) Figure 5. (a) Circle and horizontal defective WBM; (b) spectral reconstruction of the distance matrix of (a); (c) density estimation of the distance matrix of (a); (d) half defective WBM; (e) spectral reconstruction of the distance matrix of (d); (f) density estimation of the distance matrix of (d).
Based on the matrix composed of the statistics and upper control limit, a spectrum-based 3D spatial control chart was constructed. The spatial SPC implemented from the WBM image given in Figure 5 is provided in Figure 6, where three axes, X, Y, and Z, stand for the column, the row index of windows, and the control statistics. Because the defective region had a greater distance from the background, it would have higher values of the control statistics, and the statistics exceeding UCL were detected as faulty regions. The plane for UCL could be generated to detect the faulty location Figure 6. Wavelet spectrum-based 3D spatial control chart for the half-defective WBM in Figure 5.
The result showed that WS-2DSPC conducted the detection of the faulty region and calculated the degree and strength of the defects with an efficient computation speed (it took approximately 2.17 s for a single image with a size of 2 10 × 2 10 on an Intel ® Core™ i-5 6400 CPU @ 2.70 GHz and 8.00 GB of RAM, including the feature exportation and visualization). If the application of WS-2DSPC can be extended to multiple online images in a timed sequence, a system for stereotaxic and spatial monitoring, as well as the diagnosis of defects can be implemented without a bottleneck in the manufacturing process. Figure 6. Wavelet spectrum-based 3D spatial control chart for the half-defective WBM in Figure 5.

Conclusions
The result showed that WS-2DSPC conducted the detection of the faulty region and calculated the degree and strength of the defects with an efficient computation speed (it took approximately 2.17 s for a single image with a size of 2 10 × 2 10 on an Intel ® Core™ i-5 6400 CPU @ 2.70 GHz and 8.00 GB of RAM, including the feature exportation and visualization). If the application of WS-2DSPC can be extended to multiple online images in a timed sequence, a system for stereotaxic and spatial monitoring, as well as the diagnosis of defects can be implemented without a bottleneck in the manufacturing process.

Conclusions
In this article, a novel approach for spatial SPC based on wavelet spectrum analysis was proposed. With a brief review of existing approaches, we suggested an automated and quantified control method using an image processing method to detect spatial fault images in the manufacturing process without any assumptions about pre-determined values. In this procedure, 2D DWT and wavelet spectrums were utilized to calculate the degree of defects via Hurst exponents for divided windows. After the construction of the distance matrix derived from Hurst exponents was completed, the statistics and control limits were defined by the values and densities of matrix elements. The construction of the spatial control chart provided computational efficiency in diagnosing the status of images from both the spatial and real-time perspective. Through the applications of the proposed method to real-world WBMs in the semiconductor industry, the quantitative evaluation criteria for fault detection were provided in order to represent the degree of faults in WBM. Moreover, the spatial monitoring for detailed fault types led to real-time diagnosis and detection during the operation, where the cost for damage delays could be reduced and the most frequent fault type could be investigated.
For future research, classification based SPC would be possible since the defective patterns of images have typical shapes or positions, where the eventual cause of specific defects is prevented. In addition, faults in hidden layers that consist of the WBMs could be detected; while the modeling of defects in hidden layers is a valuable topic for additional research, this paper only covered the faults of image-type data that could be observed from the WBMs. It is also necessary to determine an effective control limit for automated detection, instead of the pre-specified threshold. Another approach applied to other signal processing methods could be also an interesting topic for study.