A gap study between industry expectations and current competencies of bachelor’s degree graduates in industrial engineering in Thailand 4.0 era: A case study of industrial engineering graduates of Khon Kaen University

Abstract The purpose of this research is to study the gap between industry expectations and the current competencies of bachelor’s degree industrial engineering graduates in Khon Kaen University in Thailand 4.0 era so that the research results of this study may be used as the guideline for improvement of the existing Bachelor of Engineering Degree program in Industrial Engineering in Khon Kaen University. A literature survey was conducted to determine appropriate indicators for assessing Thailand’s readiness for Industry 4.0. These indicators were further screened by 17 industrial experts using the Delphi method. Twenty-five competency performance indicators were finally adopted and sent in questionnaires to 71 industrial factories and 60 industrial engineering engineers freshly graduated from Khon Kaen University to rate the competency indicators with scores ranging from 1–5. The data were collected and analyzed to determine the gaps between the industry expectations and the current competencies of the graduates. The results of the analysis revealed that the greatest gaps were found, in descending order, delivery management, data usage, and data analytics in the usage phase. These are the areas which must be closely considered when the study program is revised. On the other hand, the smallest gaps were found, in ascending order, for innovation management, cloud usage, ICT add-on functionalities and technology in production, indicating the strength of the industrial engineering degree program of the case study university.


Introduction
The fourth industrial revolution, also known as Industry 4.0, began in 2013 in Germany with the goal of incorporating digital technology and information technology into the manufacturing processes of industrial products by integrating the data of customers' requirements with the machines used in the production process (Motyl et al., 2017) and production efficiency is enhanced by the digital technology, transforming such a factory to a smart factory. Under the concept of Industry 4.0, horizontal integration is adopted by linking the data between the factory and other organizations such as suppliers of goods or raw materials as well as customers, whereas vertical integration is used in linking data between units within the factory via digital technology. In this way, the production system and all other systems are connected throughout the complete supply chain. Furthermore, other important technologies are used for precise production and improved productivity such as automation robots, simulation models, system integration, internet of things, cyber security, cloud computing, additive manufacturing, augmented reality, and big data (Mourtzis et al., 2018).
Presently, Thailand has entered the Industry 4.0 era. Under Thailand Industry 4.0, a 20-year national strategic plan (2017)(2018)(2019)(2020)(2021)(2022)(2023)(2024)(2025)(2026)(2027)(2028)(2029)(2030)(2031)(2032)(2033)(2034)(2035)(2036) has been adopted by the Thai government in the hope to lead the country to security, wealth, and sustainability. Under the plan, the country's economy will be restructured and driven by technology and innovation. The service industry will be reformed from basic service industry to one that employs high skills. The objective of the plan is to overcome the middle-income trap to become a high-income country with improved wealth distribution. However, to realize the objective of the plan, important components of Industry 4.0 must be developed. This requires cooperation from all concerned sectors such as government, private enterprises and educational institutions. On the part of the educational institutions, they are responsible for human resource development to have the qualifications and qualities required by the industry (Benešová & Tupa, 2017).
Industrial engineering is a professional engineering discipline which deals with improvement of complex engineering production processes and systems. It involves design, development, planning, control, work research, management, and overall system monitoring and evaluation. It deals with every aspect of engineering production including personnel, data and information, machines, materials, energy, finance, and analysis and synthesis of data leading to engineering design of innovative products and services (Kádárová et al., 2014). Furthermore, industrial engineering also has an important role in advancing new knowledge of the discipline and using professional knowledge and skills in the development of efficient production systems consistent with the policy of Thailand 4.0.
For the purpose of assuring the quality and skills of industrial engineering graduates to meet the requirements of the industry in the era of Industry 4.0, the researchers are interested in determining the gap between the quality and skill requirements of the industry and the attributes which university graduates obtain from the universities by studying a case of industrial engineering graduates of Khon Kaen University, Thailand, It is expected that the results from this study may serve as the guideline in the revision of the existing Bachelor of Engineering program in industrial engineering at Khon Kaen University or in the development of a new study program. The results may also prove useful for other similar study programs in other universities in Thailand or elsewhere. In this study, important indicators which reflect industry's expectations of industrial engineering graduates were studied and collected from the literature. These indicators were then used in the construction of a questionnaire by Delphi method. The obtained questionnaire was then distributed to industrial experts representing various industries and freshly graduated industrial engineers from Khon Kaen University for rating of each indicator. The gaps between the expectations of the industry and the current competencies of the engineers were determined to be the differences between the rating scores of the industries and those of the engineers. Understanding these gaps will aid in the development of curriculum for educational institutions, ensuring that students fulfill the criteria of the industrial sector and are of adequate quality to efficiently drive the economy of the country in the industry 4.0 age.

Objectives
To study the disparity (gap) between industry expectations and the existing level of skills of industrial engineering graduates in Thailand by using the Bachelor of Engineering program in industrial engineering of Khon Kaen University as a case study.

Population and sample group
This is a quantitative research. The population of this study were those manufacturing factories which employ industrial engineering graduates of Khon Kaen University and freshly graduated industrial engineers of Khon Kaen Universities. During 2014-2018, statistics of Khon Kaen University revealed that there were 85 such factories, and there were 70 freshly graduated industrial engineers in 2018. Sample sizes were calculated as a case with known population (Yamane, 1973) at the confidence level of 95%. The sample size for the factories was determined to be 71 factories, and the factories which were classified into different sectors by the Securities and Exchange Commission of Thailand were selected by stratified random sampling. Simple random sampling with proportional representation was used to determine the factories from each sector with the results that follow, 46 factories from industrial product sector, 12 factories from technology sector, 4 factories from agricultural and food industry sector, 3 factories from consumption sector, 3 factories from real estate and construction sector, and 3 factories from service sector, totaling 71 factories. The sample size of the industrial engineering graduates was calculated to be 60 graduates, and they were randomly sampled.

Research tool
The research tool was questionnaires for data collection. The research was divided into two parts, 1) screening of competency indicators by Delphi method, 2) Gap analysis between industry expectations and current competencies of the graduates. Details of the two parts follow.
(1) Screening of indicators by Delphi method Delphi method is a tool used for reaching a decision or conclusion systematically with a clear methodology without face-to-face consultation. Participating experts are required to respond to at least two rounds of questionnaires. In each round, the researchers prepare the questionnaire for the next round by considering the majority or the average rating of the responses of the previous round. The questionnaire survey will be terminated when the consensus on the issues of interest is reached (Rowe & Wright, 1999). The method has widely been used in business, politics, defense, economics, public health, and education. It is believed that the answers or solutions obtained from the last round of the survey have been refined to be correct and appropriate because they have been screened to be the consensus of each round by the experts (Jensantikul, 2017). The use of Delphi method for screening the indicators for use in the questionnaire to determine the expectations of the industry in Thailand and competencies of industrial engineering graduates from the case university is described below.
In the first round of data collection, an open-end questionnaire was given to 17 experts. The number of 17 experts is considered sufficient. This number was used according to the Delphi method. Based on this method, Macmillan (1971) proposed Table 1 for determining the number of experts so that the decreasing rate of error is minimal (McMillan, 1971). According to the table, when the decreasing rate of error of 0.02 is required, the number of experts must be 17-21 persons. Hence, 17 experts are adequate for the purpose of this study. These experts, who were selected by purposive sampling, proportionally represented the six industrial sectors previously described, 10 experts from the industrial product sector, 3 experts from technology sector, 1 expert from agricultural and food industry sector, 1 expert from consumption sector, 1 expert from real estate and construction sector, and 1 expert from service sector. The questionnaire used in this round was prepared using the indicators describing the readiness of the industry for Industry 4.0 from literature survey. The responses of the participants in the first round gave the results of content analysis of every indicator, and the indicator with similar content or meaning to another indicator would be removed from the list.
In the second round of the Delphi method, a closed-end questionnaire was developed further from the first round for use with the same group of experts. The questionnaire contained questions which led to the selection of indicators suitable for constructing a questionnaire for the next round. Based on the rating scale from 5 to 1 (5,4,3,2,1), the experts were asked to give rating to the indicators in the questionnaire. The rating scale of 5 means the indicator is the most suitable for use in the questionnaire, whereas the rating scale of 1 means the indicator is the least suitable. Any indicator with the average score of less than 3.00 would be dropped off the list (Flanders, 1988).
After the completion of the second round, the indicators with the rating score of 3.00 or greater would be further used in the questionnaire to be confirmed by the same group of experts in the third round. The indicators which survived the third would be used in the questionnaire to be distributed to the sample groups of industries and industrial engineering graduates for data collection.
(2) Gap analysis between the industry expectations and the current competencies of industrial engineering graduates of the case study university The questionnaire so obtained was then used to collect data on the expectations of the industry and on the current competencies of the industrial engineering graduates from the case study university. The questionnaire contains closed-end questions for Section 1 and Section 2, and openend questions for Section 3 as described below.
Section 1 contained questions on general information of the responders such as education, position, and work experience.
Section 2 contained questions on knowledge and skills in various aspects related to Industry 4.0. The total number of questions in the questionnaire was 25. Both sample groups used the same questionnaire.
Section 3 contained an open-end question to which the responders were entitled to add comments and/or recommendations.

Quality assessment of the research tool
The tool used for data collection in this research was constructed by the researchers, and it was scrutinized by five experts for face validity by considering index of congruence (IOC) between each question and its objective. The IOC method is a method for determining validity of the questionnaire (Index of Item Objective Congruence: IOC) which can be calculated from where IOC = Index of Item Objective Congruence ∑ R = summation of experts' scores N = the number of experts Scoring criteria are as follows: The score = +1 if the question measures the attribute.
The score = 0 if the expert is not sure whether the question measures the attribute.
The score = −1 if the question does not measure the attribute.
Index interpretation IOC ≥ 0.50 means the question measures the attribute.
IOC < 0.50 means the question does not measure the attribute.
IOC has the value between −1 and 1 (Rovinelli & Hambleton, 1977). The questions with the IOC of 0.5 and above were considered fit for trial use with 30 industrial engineering graduates to test for reliability by determining internal consistency using Cronbach's Alpha method. The question passed the test if the internal consistency was greater than 0.7 (Nunnally, 1978). The overall coefficient of confidence of the questionnaire was 0.96. It was therefore expected that the questionnaire could be used with sufficient trustworthiness for the purpose of this research.

Data interpretation
Because the questions in Section 2 were on a rating scale, there were five possible response levels to pick from: the highest, high, moderate, low, and the least corresponding to 5, 4, 3, 2, and 1, respectively. Since the highest and the least rating scores were 5 and 1, respectively, the response range was therefore 5-1 =4. Thus, the interval between two adjacent levels of the rating is 4/5 which is 0.8. It was then possible to determine the intervals of average scores for interpretation of the data as follows.
The average of rating score between 1.00 and 1.80, means the lowest level, The average of rating score between 1.81 and 2.60, means low level, The average of rating score between 2.61 and 3.40, means moderate level, The average of rating score between 3.41 and 4.20, means high level, and The average of rating score between 4.21 and 5.00, means the highest level.

Statistical analysis
Analyses of the data were conducted by using a statistical package. Descriptive statistics was used to calculate frequency, percentage, mean, and standard deviation. Table 2 lists 20 categories of indicators pertaining to readiness for Industry 4.0 from the literature. Based on the frequency of occurrence in the literature, 7 categories were selected for screening by the Delphi method, namely, strategy and organization, smart factory, smart operation, smart products and services, data driven services, employee, and technology. Since one of the sample group of this study was intended to be newly graduated industrial engineers who did not have the

Literature search of indicators pertaining to the industry 4.0 readiness assessment
Horvat et al., Nick et al., Machado et al., Frequency status of employee, the employee category was therefore discarded. Thus, only 6 categories of indicators were screened by the Delphi method.
The researchers next classified the indicators into the categories that would be screened by using the Delphi approach. The data collected from various studies are displayed in Table 3, where the numerical values in [] after the indicators indicate the references in which the indicators were used to measure Industry 4.0 readiness.

Screening of the indicators by the Delphi approach
1)The findings of the first round of data collection The results of round 1 data collection using an open-end questionnaire to gather data from the experts indicated the possibility of using 5 categories in a questionnaire to collect data on the expectations of the industry and the current competencies of the graduates. These 5 categories consisted of 35 indicators, viz, 8, 4, 6, 11, and 6 indicators in strategy and organization, smart factory, smart operation, smart products and services, and technology, respectively as shown in Table 3.
2) The findings of the second round of data collection The findings of the second phase of data collection, which used a closed-end questionnaire to elicit responses from the same set of experts, revealed that there were only 25 indicators that met the requirements for expert evaluation. The means and standard deviations of the 17 experts' assessments are displayed in Table 3.

3)Summary of the experts' consensus in the third round
The findings of the second phase of data collection and analysis were given to the same group of experts for confirmation of the appropriateness of the indicators. All 17 experts agreed that a total of 25 indicators, as shown in Table 3, could be utilized to develop a questionnaire for gathering information on industry expectations and the existing levels of competency of the graduates of the case study university.

Industry expectations and present levels of competency of industrial engineering graduates from the case study university in the era of industry 4.0
It was discovered through the data collection from the questionnaires sent to each sample group that the industries expected industrial engineering graduates to have high skill levels in all 25 indicators. While the sample of industrial engineering graduates in the case study university demonstrated a moderate level of current skills in all areas, as seen in Table 4.

The gap between industry expectations and current levels of competency of industrial engineering graduates from the case study university in the industry 4.0 era
The overall analysis of the gaps between the levels of industry expectations and the current levels of competency of graduates in the field of industrial engineering, revealed that the top three competency gaps were in delivery management (gap = 1.07), data usage (gap = 1.04), and data analytics in usage phase (gap = 1.03), as seen in Figure 1.
On the other hand, there were four areas in which the gaps were the least, namely, innovation Management (gap = 0.42), cloud usage (gap = 0.55), ICT Add-on functionalities (gap = 0.58) and technology in production (gap 0.62) as shown in Figure 1.
From the analysis of the overall expectations of the industry in Thailand and the current levels of competency of the industrial engineering graduates of Khon Kaen University, it was found that delivery management was the most important indicator since the gap between the industrial expectation and the current level of competency of the graduates was the greatest (gap =1.07).  Failed ----*Note: In round 2, indicators with an average of less than 3.00 will be discarded, while those with a mean of 3.00 and above will be included in the third-round questionnaire.

Level of Expectation
x S.D.  Thus, the educational institutes must place importance on this indicator in their attempt to revise the curricula of the Bachelor of Engineering Program in industrial engineering so that the quality of the graduates are consistent with the requirements of the industry.

By-category analysis of differences between the mean scores of industry expectations and the existing levels of competency of graduates in industrial engineering by the statistical t-test
The analysis of differences between the mean scores of industry expectations and the existing levels of competency of graduates in industrial engineering by using the statistical t-test disclosed that the levels of industry expectations and the current levels of competency of the graduates were significantly different in all 25 categories at the significance level of 0.01 as shown in Table 4. A t-test is a standard statistical test for comparison of the averages of the data of two different sample groups. In this study the t-test was performed by using the SPSS software.

Discussion and conclusion
The research analyzed the gaps between the expectations of Thai industry and current competency levels of industrial engineering graduates in the era of Thai Industry 4.0. It has been discovered that the competency gaps between the industry expectations and the actual competencies of the graduates are the greatest in three areas including delivery management, data usage, and data analytics in usage phase. Delivery management is the action of deploying efficient logistics processes, powered by digital tools, to ensure that goods are effectively and efficiently moved from one place to another until it reaches the end-customers (The FarEye Growth Story, 2022). Data usage refers to the collection of data from the manufacturing processes for various uses, for example, predictive maintenance, production and logistics, and quality management. Data analytics in usage phase is the process of analyzing a company's big data and finding useful interrelationships that support the company's activities. Given the enormous volumes of data in When this finding was viewed in the light of the current Bachelor of Engineering program in industrial engineering of the case university, it was found that competency of the graduates in delivery management was derived from curriculum of a course in logistics which covers determination of location, demand forecast, supply chain management, acquisition of resources, transportation and distribution, inventory control, storage and transfer, logistics network planning, and analysis and control of logistics cost (Bachelor of Engineering Program in Industrial Engineering, 2020). Even though the course in logistics is one of the compulsory core courses, the curriculum of the course should be revised periodically to meet the changing requirements of the Industry 4.0 era by incorporating the content essential for increasing or improving skills in digital technology and information technology so that the graduates are sufficiently prepared to meet the challenge of Industry 4.0 (Pimdee et al., 2017). It was also found that none of the courses in the Bachelor of Engineering program had content that dealt with data usage and data analytics in usage phase, or skills in using and analysis of big data. This is consistent with the research by Pattanapairoj et al. (Pattanapairoj et al., 2021) who proposed that a Master's degree program be revised and added with more courses which match with the requirements of Industry 4.0. This proposal is in agreement with Ellahi et al. (Ellahi et al., 2019) who suggested the inclusion of Big Data Analytics in the study programs of the universities producing graduates to serve Industry 4.0.
On the other hand, the areas in which the gaps between the industry expectations and graduates' current competencies were the least included innovation management, cloud usage, ICT add-on functionality and production technology. It demonstrated that the graduates' competencies in technology and innovation, which are necessary for Industry 4.0 era were close to the industry expectation level.

Research recommendations
The findings of this research can be utilized to aid educational institutions in developing curriculum that are more responsive to the demands of industrial engineering graduate users in the Industry 4.0 era. Curriculum design or development in any case should provide opportunities for graduate users and students to voice their thoughts or common requirements to enable educational institutions to provide courses that are tailored to the needs of students, graduates and concerned industry. All the parties involved in the redesign of the curricula of the Bachelor of Engineering Program in industrial engineering must pay great attention to the indicators with the greatest gaps between the expectations of the industry and the actual capacities of the current graduates, previously discussed.
Equipment Infrastructure means all items of equipment and associated interfaces adapted or intended for use, inter alia, in the provision of a telephone service to a terminal device but excluding intermediate products.
Data Usage means statistical data, analytics, trends, and usage information derived from each Unify Cloud Services User's use of Unify Cloud Services. Data Usage includes, by example and without limitation, aggregated quantitative information about number of Cloud Services User, used bandwidth, storage space or CPU capacity.
IT Systems mean all electronic data processing, information, recordkeeping, communications, telecommunications, account management, inventory management and other computer systems (including all computer programs, software, databases, firmware, hardware, and related documentation) and Internet websites.
Cloud Usage is not a single computer but a virtual "computing cloud" consisting of many interconnected computers. Users do not need to be on site to access cloud-based computers.
IT Security is intended to prevent the manipulation of data and systems by unauthorized third parties. The meaning behind this is that socio-technical systems, i.e., people and technology, within companies/organizations and their data are protected against damage and threats.
Information Sharing describes the exchange of data between various organizations, people, and technologies. There are several types of information sharing: Information shared by individuals, Information shared by organizations, Information shared between firmware/software. Intelligent Lots is a technology-driven approach that utilizes Internet-connected machinery to monitor the production process.
ICT Add-on Functionalities is all devices, networking components, applications and systems that combined allow people and organizations (i.e., businesses, nonprofit agencies, governments, and criminal enterprises) to interact in the digital world.

Data Analytics in Usage
Phase is the process of analyzing a company's big data and finding useful interrelationships that support the company's activities. Given the enormous volumes of data in businesses today, data can only yield an added value if it can be placed in context and consolidated under larger categories.
Digitalization of Products is a software enabled product or service that offers some form of utility to a human being. In other words, all digital products, from a mobile app to a website experience, attempt to solve a problem for a group of people trying to accomplish something.
Predictive Maintenance are designed to detect machine errors such as interruptions or outages before they happen. The aim is to prevents errors through maintenance and proactive repairs.

Raw Materials' and Finished
Products' Inventory Management refers to the process of ordering, storing, using, and selling a company's inventory. This includes the management of raw materials, components, and finished products, as well as warehousing and processing of such items.
Delivery Management is the action of deploying efficient logistics processes, powered by digital tools, to ensure that goods are effectively and efficiently moved from one place to another until it reaches the end-customer.
Flexibility of Product Characteristics is as the amount of responsiveness (or adaptability) for any future change in a product design, including new products and derivatives of existing products.