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A Conceptual Framework for “Industry 3.5” to Empower Intelligent Manufacturing and Case Studies
Available online at www.sciencedirect.com
ScienceDirect Procedia Manufacturing 11 (2017) 2009 – 2017
27th International Conference on Flexible Automation and Intelligent Manufacturing, FAIM2017, 27-30 June 2017, Modena, Italy
A Conceptual Framework for “Industry 3.5” to Empower Intelligent Manufacturing and Case Studies Chen-Fu Chien*, Tzu-yen Hong, and Hong-Zhi Guo Department of Industrial Engineering & Engineering Management, National Tsing Hua University 101 Section 2 Kuang Fu Road, Hsinchu 30013, Taiwan
Abstract Leading nations have reemphasized manufacturing with national competitive strategies such as Industry 4.0. The paradigm of production and manufacturing system is shifting, in which the increasing adoption of intelligent equipment and robotics, Internet of Things (IOT), and big data analytics have empowered manufacturing intelligence. Leading companies are battling for dominant positions in this newly created arena via providing novel value-proposition solutions and/or employing new technologies to enhance smart production. However, most of emerging countries may not ready for the migration of Industry 4.0. This study aims to propose a conceptual framework of “Industry 3.5” as a hybrid strategy between Industry 3.0 and to-be Industry 4.0, to address some of the needs for flexible decisions and smart production in Industry 4.0. Empirical studies in hightech manufacturing and other industries are used for illustration. Future research directions are discussed to implement the proposed Industry 3.5 to facilitate the migration of Industry 4.0. © 2017 byby Elsevier B.V.B.V. This is an open access article under the CC BY-NC-ND license © 2017 The TheAuthors. Authors.Published Published Elsevier (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the scientific committee of the 27th International Conference on Flexible Automation and Peer-review under responsibility of the scientific committee of the 27th International Conference on Flexible Automation and Intelligent Manufacturing. Intelligent Manufacturing Keywords: Smart Production; Semiconductor Manufacturing; Industry 3.5; Big Data; Flexible Decision; Manufacturing Intelligence
* Corresponding author. Tel.: +886-3-5742648; fax: +886-3-5722685. E-mail address: [email protected]
2351-9789 © 2017 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the scientific committee of the 27th International Conference on Flexible Automation and Intelligent Manufacturing doi:10.1016/j.promfg.2017.07.352
2010
Chen-Fu Chien et al. / Procedia Manufacturing 11 (2017) 2009 – 2017
1. Introduction The leading nations including Germany and USA have reemphasized the importance of manufacturing in the corresponding national competitive strategies such as Industry 4.0 and AMP. The paradigm of production and manufacturing system is shifting. Unlike the existing manufacturing system in which mass production remains as the main strategy, Industry 4.0 is built upon the Cyber-Physical System (CPS), it also includes the implementation of Internet of Things (IoT) and the concept of smart factory. Under the concept of Industry 4.0, CPS integrates the visual and the real world, the real time monitoring and collaborating will be achieved. Through the application of IoT, all machines in the factories are connected as a network, which allows information exchange and collaboration to achieve a flexible, self-adaptive production in the entire supply chain. Moreover, Industry 4.0 is able to fulfill mass customization, to make internet-based optimal decision and diagnostics, and finally, to achieve self-learning, self-aware and self-optimize by the integration of information and technologies. As the mentioned projects proposed by the leading countries, manufacturing intelligence has become a global trend. However, some countries such as Taiwan, in which the infrastructures of industry are not well developed as the leading countries, it is unrealistic to hope that the domestic industries can leap up to US or Germany in a short term. It becomes a crucial issue for these countries to develop a suitable strategy to fit in the storm of manufacturing intelligence. Focusing on the characteristics of high-tech industries and practical problems in Taiwan, this study aims to propose a hybrid strategy of Industry 3.0 and 4.0 with disruptive innovations, called Industry 3.5. It integrates the concept of digital decision, smart supply chain, total resource management and smart manufacturing, use big data analysis and optimization approaches as the main tools, develop a framework which is suitable for existing manufacturing systems and industrial patterns in Taiwan [1]. An empirical study was conducted in a leading TFTLCD manufacturing company in Taiwan for validation. In particular, a daily planning and scheduling (DPS) system that integrates a Genetic algorithm (GA) based scheduling system to maximize the bottleneck utilization and a fabwide simulation-based scheduling system with different dispatching rules for non-bottleneck machines to fulfill and maximize the daily output target of array factory. The real time operational decisions will be generated based on the output of the system. The empirical study shows the benefit for the implementation of Industry 3.5 in the part of smart production. 2. Industry 3.5 This study proposed a conceptual framework of Industry 3.5 (see Fig. 1) with five features including digital decision, smart supply chain, smart manufacturing, total resource management, and smart factory. 2.1. Digital decision Firstly, digital decision is developed to support flexible decisions in situ. In Industry 3.0, most of the operational decisions are made according to the experiences of managers. However, how to analyze the collected information and provide the context and meaning to help the users to make better decisions is a crucial issue for the current manufacturing systems. The collected data in the current manufacturing process are characterized by volume, variety, and velocity. Though data collection is simple with the application of smart sensors, without the data analysis, the collected data is useless. It is necessary to calculate, storage and analyze the data to make it meaningful. By the big data analysis and cloud computing approach, some of the characteristics or information are look forward being discovered. The development of digital system and decision support system will transform the information into the visualization interfaces. The manager can make operational strategies with the help of the provided information. 2.2. Smart Supply Chain The supply chain of the industry nowadays becomes more and more complex. In Industry 4.0, the entire supply chain will be connected as a network through the implementation of IoT. With the highly adaptive networks, the
Chen-Fu Chien et al. / Procedia Manufacturing 11 (2017) 2009 – 2017
issues such as demand forecasting, capacity planning, or logistics will not be worried. However, the development of supply chain still remains in the traditional supply chain management. To maintain the competitiveness of the industry, the strengthening of the supply chain management is crucial. Big data analysis is supplied to help the manager to select the most beneficial order. Through the data analysis of customer’s response for different prices, the most profitable pricing strategies can be made. Simultaneously, with the data of customer behavior, the customer’s requirements can be understood and provide the directions of product design and innovation. Moreover, big data analysis can forecast the future demand to enhance the capital effectiveness and profitability. It has been examined to be useful in the construct of a multi-generation diffusion model for the demand forecast of semiconductor products [2]. For capacity planning, high technology industries, such as semiconductor manufacturing, are usually progressing in a highly uncertain environment. To avoid the condition of oversupply, a mini-max regret strategy with the consideration of possible outcomes of multi-period demand is proposed to improve the capacity utilization and capital effectiveness.
Fig. 1. Conceptual framework of Industry 3.5
2.3. Smart Manufacturing As mentioned previously, machines of Industry 4.0 allows being self-adaptive, self-configure and self-optimize to achieve the flexible and adaptive manufacturing. However, the basic process control methods have still been used as the main tools in most existing manufacturing environments. Meanwhile, most of the companies still focus on mass production to achieve economies of scale. The purpose of smart manufacturing is to improve the production and quality performance of existing manufacturing environments with appropriate methods. The improvements include the flexibility to produce diverse products with small lot size, and the ability to enhance the productivity as well as the yield rate. Some empirical studies used different ways to achieve of smart manufacturing have been done as follows. A big data analytics method was proposed to construct a framework to detect the root causes for subbatch processing with relatively high catching accuracy and small errors [3]. Advanced process control is also widely adopted in the previous studies. A dynamically adjusted proportional-integral run-to-run controller, in which the consideration of future disturbance prediction was applied to reduce overlay errors effectively [4]. Chien et al.
2011
2012
Chen-Fu Chien et al. / Procedia Manufacturing 11 (2017) 2009 – 2017
[5] integrated a feed-forward run-to-run controller with the mini-max regret tool dispatching rules for the process control of semiconductor manufacturing to reduce the overall variation of linewidth caused by the tool misalignment between the photolithography and etching processes. 2.4. Total Resource Management Resource is one of the basic requirements to maintain the operation of a company. Many studies focus on the different techniques to manage different resources. However, in order to integrate the operational strategies and the usage of resources, it is necessary to implement the construction of managerial methods for total resource and the corresponding evaluation of performance index. Thus, the term “total resource management” can be defined as the method to enhance the effectiveness of productivity and resource utilization through systematic management architecture. Based on the industry environment and the requirements, the manager will set the fundamental objectives and construct the performance indicators of the resources for the factories in the firstly. After determine the fundamental objectives, the means objectives, which are the tools or methods to help the manager to reach the fundamental objectives, will be constructed. Through the performance evaluation, the performance indicators of the resources will be calculated to response the achievement of the fundamental objectives. A number of indicators such as Overall tool Group Efficiency and Overall Wafer Effectiveness were developed for monitoring operational performance to empower smart production. Thus, the review and improvement, based on the performance evaluation, is to improve the quality of decision and update the fundamental objectives. The step of review and improvement has to be a continuous and periodic job to enhance the competitiveness of resource management. 2.5. Smart Factory As mentioned previously, the target of Industry 4.0 is to build a smart factory, in which machines are adaptable, flexible, self-adaptable and self-learning. However, the smart factory of Industry 3.5 focuses more on the short-term improvement of the existing environments. Aiming to empower the manufacturing intelligence, the big data analysis and optimization tools are applied to each level of operations, including operational strategy, supply chain management, shop floor control, and quality control. By the integration of the above-mentioned components, a smart factory of Industry 3.5 is not just a factory with high-level automation, but also a factory having the ability of decision-making. In order to fulfill the decision-making, the ability of data collection and analysis as well as the digitalization and systemization of user interfaces, are necessary for the implementation of Industry 3.5. Thus, the upgrade of manufacturing system is focused not only on hardware, but also on the integration of software, the experience of management, and development of analysis abilities. Furthermore, along with the development of IoT and CPS, the strengthening of the data analysis ability, and the improvement of the infrastructure, all machines in the smart factories of Industry 3.5 are expected to connect and communicate each other to achieve the vision of smart factories of Industry 4.0. A comparison of Industry 3.0, 3.5 and 4.0 are shown as Table 1. Table 1. Comparisons among Industry 3.0, 3.5 and 4.0 Features
Industry 3.0
Industry 3.5
Industry 4.0
Core Concept
Highly automated system
Decision making ability with the improvement of existing environments
Smart factory with CPS and IoT
Production Strategy
Mass Production
Flexible Manufacturing (diverse products with small lot size)
Mass Customization
Quality Control
Statistical Process Control
Advanced Process Control
Self-aware; Self-predict
Resources Management
Materials Management; Human Resource Management; etc.
Total resource Management
Self-configure; Selfoptimize
Development Priorities
Investment of hardware
Integration of ability of data analysis and experience of management
Construction of CPS and IoT
Chen-Fu Chien et al. / Procedia Manufacturing 11 (2017) 2009 – 2017
3. Empirical study 3.1. Problem structuring An empirical study was conducted for TFT-LCD array manufacturing to illustrate the proposed framework. The manufacturing process of TFT-LCD can be divided into three main processes: thin film transistor array fabrication, liquid crystal assembly, and module assembly process. TFT array fabrication has the longest production cycle time. The processes in TFT array fabrication are similar to semiconductor manufacturing. The main stages of TFT array include cleaning, deposition, photolithography, etching, stripping and inspection with five to eight times re-entrant. The total manufacturing process required for a typical TFT array is about 30~50 steps. The manufacturing process of photolithography, including the processes of coating, exposure and developing, is usually the bottleneck among the entire array production processes. The complex constraints to be considered in the photolithography process, include the photo masks needed in the scanner machines, the constraints of available machines for each product in each layer, and the limited waiting time of work in process (WIP). The photo mask is the second resource of exposure process in additional to the scanner machines, and the exposure process cannot work without the usage of photo masks even though the scanner machines are available. Typically, every product in each layer requires a unique photo mask. Because the photo mask is expensive, one product generally has one set of masks only. Thus, the transportation time of photo masks between each machine also has to be considered. The available machines for each product in each layer are another constraint of photolithography process. Due to the quality issue, some of the photo masks are not allowed to use in some specific scanner machines. In turns, the products which require those photo masks will not allowed to process in those specific scanner machines. The available machines for each product is thus changed dynamically based on the condition and constraints of machines, and it will be updated daily. Moreover, if the WIP in the photolithography stage exceeds 24 hours, the products have to be reworked due to the quality consideration. In order to avoid rework, generally the factory will set a limited waiting time, which cannot be exceeded. The scheduling problem is therefore complex after considering the mentioned constraints. The utilization of photolithography depends highly on scheduling, by which the achievement of daily output target will also be impacted. In practice, the users have to maximize the utilization of photolithography stage with the consideration of complex constraints on one hand; they have to fulfill the daily output target of each product on the other hand. Therefore, most of the scheduling plan of TFT array manufacturing still highly depends on user’s experiences. The schedule plan will be scheduled and changed dynamically every day. Because of the highly uncertain schedule plan, it is difficult to forecast the daily throughput by an effective way. A multi-objective hybrid genetic algorithm (MO-HGA) to address the TFT-LCD module assembly scheduling problem by simultaneously considering the objectives, such as makespan, the weighted number of tardy jobs, and the total machine setup time [6]. Indeed, the proposed DPS integrated a GA-based scheduling system to maximize the utilization of photolithography stage under complex constraints and the simulation-based scheduling system to maximize the daily output target fulfillment of TFT array factory. By the results of scheduling, suggestions of scheduling and dispatching decisions can be made by the system. It follows the concept of industry 3.5 to make valuable improvement in the existing manufacturing system as listed in Table 2. Table 2. The comparisons between original factory and implementation of DPS system Features
Original factory
Implementation of DPS system
Automatic Level
Scheduled by the users; artificially.
Scheduled by the system; automatically.
Efficiency
Difficult and slow to schedule the plan
Easy and fast with the automatic computing
Flexibility
Complicated to implement new process, new
Flexible and adaptive by the usage of simulation-based
dispatching rules, and products
scheduling system
Optimal Methodology
Without optimal methodology
Genetic algorithms
Forecasting Mechanism
Without a system to forecast the throughput
Provide by the simulation-based scheduling system
2013
2014
Chen-Fu Chien et al. / Procedia Manufacturing 11 (2017) 2009 – 2017
3.2. Approach In the present DPS system, the data including the daily release plan (daily input), the WIP condition in each machines of each stage, and the technical data are all collected form the Manufacturing execution system (MES) of the company, with the following information: 1. The takt time for each product in each layer and each stage. 2. The transportation time of each stage. 3. The manufacturing process of each product. 4. The photo mask allocation, and the available machines for each photo mask. The DPS system will update the above information daily. In order to generate the real time schedule plan, the WIP condition and daily planning schedule provided the latest information of current status of TFT array factory. A schedule plan, which aims to maximize the utilization of photolithography stage, will be generated by the GA-based scheduling system. The results will be implemented into the simulation-based scheduling system. Different types of product, different manufacturing processes of different products, the difference in process time between among layers and stages, as well as the transportation time among stages will be considered in the simulation-based scheduling system. Different dispatching rules will be designed. The schedule of photolithography and the schedule of TFT array factory will be shown by these two systems. Some of the graphical data will be generated in order to help the manager to understand the situation easily. The performance analyzer is based on the difference of the throughput and output of each product to calculate the performance of each experiment. Eventually, the manager can make scheduling decisions with the help of the system. The GA-based scheduling is implemented using Java program. The simulation-based scheduling system is developed using the simulation software Rockwell Arena 14.0. The loading schedule display system and the performance analyzer are built under the environment of Microsoft Excel 2013 and Microsoft Visual Basic for Application. The architecture of the DPS system is shown as Figure 2.
Fig. 2. The architecture of the DPS system
The target of the GA-based scheduling system is to maximize the utilization of the photolithography process because it is the bottleneck of TFT array manufacturing. Among the system, some constraints will be considered as shown in Table 3. A job is defined as a batch of specific product at a layer, which will be processed in the photolithography stage. The design of the chromosome consists of job sequence part and machine assignment part. The random key sequencing is used in each part, and each gene represents each job in both parts. In the job sequence parts, a random number between zero and one will be generated for each gene, which represents the process sequence of jobs in the same machine. The gene with the less number will be processed firstly. In the machine assignments parts, a random number between zero and one will be also generated. For each job, the available machine will be predetermined. The probability of job assignment to each available machine will be calculated as the roulette wheel, in which every machine represents an interval between zero and one. If the random number of gene is in the interval of specific machine, the job will be processed in that machine. For the first population, one hundred chromosomes will be generated.
Chen-Fu Chien et al. / Procedia Manufacturing 11 (2017) 2009 – 2017
To generate new offspring, the first population will be sorted by the objective value and the best top fifty chromosomes will be picked up in order to create another fifty offspring chromosomes. The methods of crossover and mutation are used. For crossover, two of chromosomes will be paired randomly, and the one-cut point crossover and two-cut point mutation are used to generate offspring. The probability of crossover and mutation will be determined in advance. The fifty superior chromosomes of initial population and the fifty offspring generated by crossover and mutation will be population of next generation. The system will evolve continuously until one thousand generations. The best solution will be shown after the system terminated. Table 3. Constraints considered in the proposed GA-based scheduling system Considered Constraints
Explanation
Machine capacity constraints
One job can only be processed in one machine at the same time.
Job arrival time constraints
A job can be processed only if it the job has arrived. The arrival time of each job is
Photo mask constraints
The job can be processed only if the photo mask of current job is in the machine.
predetermined. Photo mask transportation time constraints
The transportation time of photo mask between each machine has to be considered. The photo masks can be used only if the photo mask is transported to specified machines. The transportation time between each machine is determined in advance.
Available machines constraints
The jobs can be processed only in the specified machines. The available machines for each job are predetermined.
Limited waiting time constraints
The waiting time of the jobs cannot exceed the limited waiting time. Otherwise a penalty will be implemented in the objective function. It is a soft constraint.
Then, the developed simulation-based scheduling system is employed to generate the scheduling plan of entire TFT array factory to maximize daily output fulfillment and fulfill the daily output target. Meanwhile, the design of the system allows to change the configuration easily to strengthen the flexible of the system. The construction of the system includes the entire TFT array manufacturing steps. Among them, the schedule plan of photolithography is implemented from the GA-based scheduling system. A job can be defined as a batch of specific product at a layer, which will be processed in the TFT array factory. The job will read the output files of GA-based scheduling system and reappear the schedule plan once it enters the photolithography stage. For other stages, the available machines for each job will be defined in advance. The job will be assigned to the available machine, in which the WIP is minimal. After the manufacturing process in the current stage finished, the job will be transported to the next stage by the predetermined manufacturing sequence until the last stage of current job. The job information including the location of current stage, the entering time of current stage, the product name, the lot size of current job and the processed machine will be recorded. The output data will input into the performance analyzer to generate the performance index. The scheduling results and the graphical data will be shown after the simulation completed. The result of GAbased scheduling system can be shown as a Gantt chart to represent the scheduling sequences of jobs in each machine of photolithography stage. Table 4 listed the improvement of the proposed GA-based scheduling system than existing approaches. For the part of simulation-based scheduling system, the predetermined dispatching rules will be experimented. Among the rules, the users also allow to define the priority of the products to accelerate the manufacturing progress of important products. The performance analyzer will analyze the scheduling results based on the throughput and the target of each product, to find a dispatching rule which maximize the output target fulfillment. A graph will be generated for each product. The throughput, the WIP condition for each step, and the difference between the throughput and output target in the end of simulation are shown. Through the above information, the users are able to understand the progress of each product. Furthermore, the output target for next three days and the accumulated value of WIP from the last step to first step are indicated. It shows the expected output amount of current WIP for next three days, and whether the current WIP is on schedule to fulfill the target of next three days. Thus, the users can make the corresponding response in advance to reach the target. A suggestion of
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Chen-Fu Chien et al. / Procedia Manufacturing 11 (2017) 2009 – 2017
priority setting of each product for next day will be generated, to accelerate the delayed products and fulfill the output target next day. Table 4. Improvement of GA-based scheduling system Performance Index
Original factory
Implementation of DPS system
Utilization of Photolithography
98%
99%
Time Required
More than 30 minutes
Less than 5 minutes
Reschedule Required
Yes
No
4. Conclusion Facing the challenges of national manufacturing strategies such as Industry 4.0 of Germany [7], Advanced Manufacturing Partnership (AMP) of USA [8], Industry 4.1J of Japan, and Made in China 2025 [9], Taiwanese companies need to find the right strategy to enhance the core competence and upgrade industry structure. However, it is unrealistic to apply the same strategies to Taiwan in the short-term since the background of the industries and the infrastructures are different. Driven by the enabling technologies and their innovative applications, this study aims to propose possible directions to support Taiwanese industries to find niche positions in the restructuring value chain and conduct an empirical study in a leading TFT-LCD company as illustration of smart production. With the integration of previous experiences and the present study, “Industry 3.5” is proposed in this study as the hybrid strategy of Industry 3.0 and Industry 4.0, to empower the manufacturing intelligence in the existing manufacturing environments of Taiwan. The DPS system of TFT array manufacturing is proposed in this study. The GA-based scheduling system and the simulation-based system are developed to maximize the utilization of photolithography stage and fulfill the daily output target of each product in the TFT array factory. This study focuses on the integration of domain knowledge and decision rules derived from big data analysis and simulation to find effective directions to improve performance of manufacturing, and achieve the decision making ability of smart factory. The results have shown practical viability of the developed solutions to enhance the productivity and flexibility. Indeed, it has been implemented in the case company. Further research can be done in various industries for upgrading existing factories and enhancing the digital decision capability of dispatching and scheduling for smart production. Acknowledgements This research is supported by Ministry of Science and Technology (MOST105-2218-E-007-028; MOST 1052218-E-007-027) and the Toward World Class University Project from Ministry of Education (105N536CE1), Taiwan. References [1] C.-F. Chien, K.-Y. Lin, J.-B. Sheu, C.-H. Wu. Retrospect and Prospect on Operations and Management Journals in Taiwan: From Industry 3.0 to Industry 3.5. J Management 33 (1)(2016) 87-103. [2] C.-F. Chien, Y. J. Chen, and J. T. Peng. Manufacturing intelligence for semiconductor demand forecast based on technology diffusion and product life cycle. IJPE 128 (2)(2010) 496-509. [3] C.-F. Chien, S. C. Chuang. A framework for root cause detection of sub-batch processing system for semiconductor manufacturing big data analytics, IEEE Trans. Semiconductor Manufacturing 27 (4)(2014) 475-488. [4] C.-F. Chien, Y.J. Chen, C.Y. Hsu, H.K. Wang. Overlay error compensation using advanced process control with dynamically adjusted proportional-integral R2R controller. IEEE Trans. Automation Science and Engineering 11 (2)(2014) 473-484. [5] C.-F. Chien, Y. J. Chen, and C. Y. Hsu. A novel approach to hedge and compensate the critical dimension variation of the developed-andetched circuit patterns for yield enhancement in semiconductor manufacturing. Computers & Operations Research 53(2015) 309-318. [6] C.-W. Chou, C.-F. Chien, M. Gen. A multiobjective hybrid genetic algorithm for TFT-LCD module assembly scheduling. IEEE Trans. Automation Science and Engineering 11 (3)(2014) 692-705. [7] Federal Ministry of Education and Research. Recommendation the Strategic Initiative INDUSTRIE 4.0. National Academy of Science and Engineering of Germany (2013).
Chen-Fu Chien et al. / Procedia Manufacturing 11 (2017) 2009 – 2017 [8] Executive Office of the President of the United States. Accelerating U.S. Advanced Manufacturing, AMP2.0 Steering Committee Report. The President.S. Advanced Manufacturing, AMP2.0 Steeringology of the United States (2014). [9] State Council of China. China Manufacturing 2025. (2015), available: http://www.gov.cn/zhengce/content/2015-05/19/content_9784.htm, last accessed: July, 7th, 2015.
2017
ScienceDirect Procedia Manufacturing 11 (2017) 2009 – 2017
27th International Conference on Flexible Automation and Intelligent Manufacturing, FAIM2017, 27-30 June 2017, Modena, Italy
A Conceptual Framework for “Industry 3.5” to Empower Intelligent Manufacturing and Case Studies Chen-Fu Chien*, Tzu-yen Hong, and Hong-Zhi Guo Department of Industrial Engineering & Engineering Management, National Tsing Hua University 101 Section 2 Kuang Fu Road, Hsinchu 30013, Taiwan
Abstract Leading nations have reemphasized manufacturing with national competitive strategies such as Industry 4.0. The paradigm of production and manufacturing system is shifting, in which the increasing adoption of intelligent equipment and robotics, Internet of Things (IOT), and big data analytics have empowered manufacturing intelligence. Leading companies are battling for dominant positions in this newly created arena via providing novel value-proposition solutions and/or employing new technologies to enhance smart production. However, most of emerging countries may not ready for the migration of Industry 4.0. This study aims to propose a conceptual framework of “Industry 3.5” as a hybrid strategy between Industry 3.0 and to-be Industry 4.0, to address some of the needs for flexible decisions and smart production in Industry 4.0. Empirical studies in hightech manufacturing and other industries are used for illustration. Future research directions are discussed to implement the proposed Industry 3.5 to facilitate the migration of Industry 4.0. © 2017 byby Elsevier B.V.B.V. This is an open access article under the CC BY-NC-ND license © 2017 The TheAuthors. Authors.Published Published Elsevier (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the scientific committee of the 27th International Conference on Flexible Automation and Peer-review under responsibility of the scientific committee of the 27th International Conference on Flexible Automation and Intelligent Manufacturing. Intelligent Manufacturing Keywords: Smart Production; Semiconductor Manufacturing; Industry 3.5; Big Data; Flexible Decision; Manufacturing Intelligence
* Corresponding author. Tel.: +886-3-5742648; fax: +886-3-5722685. E-mail address: [email protected]
2351-9789 © 2017 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the scientific committee of the 27th International Conference on Flexible Automation and Intelligent Manufacturing doi:10.1016/j.promfg.2017.07.352
2010
Chen-Fu Chien et al. / Procedia Manufacturing 11 (2017) 2009 – 2017
1. Introduction The leading nations including Germany and USA have reemphasized the importance of manufacturing in the corresponding national competitive strategies such as Industry 4.0 and AMP. The paradigm of production and manufacturing system is shifting. Unlike the existing manufacturing system in which mass production remains as the main strategy, Industry 4.0 is built upon the Cyber-Physical System (CPS), it also includes the implementation of Internet of Things (IoT) and the concept of smart factory. Under the concept of Industry 4.0, CPS integrates the visual and the real world, the real time monitoring and collaborating will be achieved. Through the application of IoT, all machines in the factories are connected as a network, which allows information exchange and collaboration to achieve a flexible, self-adaptive production in the entire supply chain. Moreover, Industry 4.0 is able to fulfill mass customization, to make internet-based optimal decision and diagnostics, and finally, to achieve self-learning, self-aware and self-optimize by the integration of information and technologies. As the mentioned projects proposed by the leading countries, manufacturing intelligence has become a global trend. However, some countries such as Taiwan, in which the infrastructures of industry are not well developed as the leading countries, it is unrealistic to hope that the domestic industries can leap up to US or Germany in a short term. It becomes a crucial issue for these countries to develop a suitable strategy to fit in the storm of manufacturing intelligence. Focusing on the characteristics of high-tech industries and practical problems in Taiwan, this study aims to propose a hybrid strategy of Industry 3.0 and 4.0 with disruptive innovations, called Industry 3.5. It integrates the concept of digital decision, smart supply chain, total resource management and smart manufacturing, use big data analysis and optimization approaches as the main tools, develop a framework which is suitable for existing manufacturing systems and industrial patterns in Taiwan [1]. An empirical study was conducted in a leading TFTLCD manufacturing company in Taiwan for validation. In particular, a daily planning and scheduling (DPS) system that integrates a Genetic algorithm (GA) based scheduling system to maximize the bottleneck utilization and a fabwide simulation-based scheduling system with different dispatching rules for non-bottleneck machines to fulfill and maximize the daily output target of array factory. The real time operational decisions will be generated based on the output of the system. The empirical study shows the benefit for the implementation of Industry 3.5 in the part of smart production. 2. Industry 3.5 This study proposed a conceptual framework of Industry 3.5 (see Fig. 1) with five features including digital decision, smart supply chain, smart manufacturing, total resource management, and smart factory. 2.1. Digital decision Firstly, digital decision is developed to support flexible decisions in situ. In Industry 3.0, most of the operational decisions are made according to the experiences of managers. However, how to analyze the collected information and provide the context and meaning to help the users to make better decisions is a crucial issue for the current manufacturing systems. The collected data in the current manufacturing process are characterized by volume, variety, and velocity. Though data collection is simple with the application of smart sensors, without the data analysis, the collected data is useless. It is necessary to calculate, storage and analyze the data to make it meaningful. By the big data analysis and cloud computing approach, some of the characteristics or information are look forward being discovered. The development of digital system and decision support system will transform the information into the visualization interfaces. The manager can make operational strategies with the help of the provided information. 2.2. Smart Supply Chain The supply chain of the industry nowadays becomes more and more complex. In Industry 4.0, the entire supply chain will be connected as a network through the implementation of IoT. With the highly adaptive networks, the
Chen-Fu Chien et al. / Procedia Manufacturing 11 (2017) 2009 – 2017
issues such as demand forecasting, capacity planning, or logistics will not be worried. However, the development of supply chain still remains in the traditional supply chain management. To maintain the competitiveness of the industry, the strengthening of the supply chain management is crucial. Big data analysis is supplied to help the manager to select the most beneficial order. Through the data analysis of customer’s response for different prices, the most profitable pricing strategies can be made. Simultaneously, with the data of customer behavior, the customer’s requirements can be understood and provide the directions of product design and innovation. Moreover, big data analysis can forecast the future demand to enhance the capital effectiveness and profitability. It has been examined to be useful in the construct of a multi-generation diffusion model for the demand forecast of semiconductor products [2]. For capacity planning, high technology industries, such as semiconductor manufacturing, are usually progressing in a highly uncertain environment. To avoid the condition of oversupply, a mini-max regret strategy with the consideration of possible outcomes of multi-period demand is proposed to improve the capacity utilization and capital effectiveness.
Fig. 1. Conceptual framework of Industry 3.5
2.3. Smart Manufacturing As mentioned previously, machines of Industry 4.0 allows being self-adaptive, self-configure and self-optimize to achieve the flexible and adaptive manufacturing. However, the basic process control methods have still been used as the main tools in most existing manufacturing environments. Meanwhile, most of the companies still focus on mass production to achieve economies of scale. The purpose of smart manufacturing is to improve the production and quality performance of existing manufacturing environments with appropriate methods. The improvements include the flexibility to produce diverse products with small lot size, and the ability to enhance the productivity as well as the yield rate. Some empirical studies used different ways to achieve of smart manufacturing have been done as follows. A big data analytics method was proposed to construct a framework to detect the root causes for subbatch processing with relatively high catching accuracy and small errors [3]. Advanced process control is also widely adopted in the previous studies. A dynamically adjusted proportional-integral run-to-run controller, in which the consideration of future disturbance prediction was applied to reduce overlay errors effectively [4]. Chien et al.
2011
2012
Chen-Fu Chien et al. / Procedia Manufacturing 11 (2017) 2009 – 2017
[5] integrated a feed-forward run-to-run controller with the mini-max regret tool dispatching rules for the process control of semiconductor manufacturing to reduce the overall variation of linewidth caused by the tool misalignment between the photolithography and etching processes. 2.4. Total Resource Management Resource is one of the basic requirements to maintain the operation of a company. Many studies focus on the different techniques to manage different resources. However, in order to integrate the operational strategies and the usage of resources, it is necessary to implement the construction of managerial methods for total resource and the corresponding evaluation of performance index. Thus, the term “total resource management” can be defined as the method to enhance the effectiveness of productivity and resource utilization through systematic management architecture. Based on the industry environment and the requirements, the manager will set the fundamental objectives and construct the performance indicators of the resources for the factories in the firstly. After determine the fundamental objectives, the means objectives, which are the tools or methods to help the manager to reach the fundamental objectives, will be constructed. Through the performance evaluation, the performance indicators of the resources will be calculated to response the achievement of the fundamental objectives. A number of indicators such as Overall tool Group Efficiency and Overall Wafer Effectiveness were developed for monitoring operational performance to empower smart production. Thus, the review and improvement, based on the performance evaluation, is to improve the quality of decision and update the fundamental objectives. The step of review and improvement has to be a continuous and periodic job to enhance the competitiveness of resource management. 2.5. Smart Factory As mentioned previously, the target of Industry 4.0 is to build a smart factory, in which machines are adaptable, flexible, self-adaptable and self-learning. However, the smart factory of Industry 3.5 focuses more on the short-term improvement of the existing environments. Aiming to empower the manufacturing intelligence, the big data analysis and optimization tools are applied to each level of operations, including operational strategy, supply chain management, shop floor control, and quality control. By the integration of the above-mentioned components, a smart factory of Industry 3.5 is not just a factory with high-level automation, but also a factory having the ability of decision-making. In order to fulfill the decision-making, the ability of data collection and analysis as well as the digitalization and systemization of user interfaces, are necessary for the implementation of Industry 3.5. Thus, the upgrade of manufacturing system is focused not only on hardware, but also on the integration of software, the experience of management, and development of analysis abilities. Furthermore, along with the development of IoT and CPS, the strengthening of the data analysis ability, and the improvement of the infrastructure, all machines in the smart factories of Industry 3.5 are expected to connect and communicate each other to achieve the vision of smart factories of Industry 4.0. A comparison of Industry 3.0, 3.5 and 4.0 are shown as Table 1. Table 1. Comparisons among Industry 3.0, 3.5 and 4.0 Features
Industry 3.0
Industry 3.5
Industry 4.0
Core Concept
Highly automated system
Decision making ability with the improvement of existing environments
Smart factory with CPS and IoT
Production Strategy
Mass Production
Flexible Manufacturing (diverse products with small lot size)
Mass Customization
Quality Control
Statistical Process Control
Advanced Process Control
Self-aware; Self-predict
Resources Management
Materials Management; Human Resource Management; etc.
Total resource Management
Self-configure; Selfoptimize
Development Priorities
Investment of hardware
Integration of ability of data analysis and experience of management
Construction of CPS and IoT
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3. Empirical study 3.1. Problem structuring An empirical study was conducted for TFT-LCD array manufacturing to illustrate the proposed framework. The manufacturing process of TFT-LCD can be divided into three main processes: thin film transistor array fabrication, liquid crystal assembly, and module assembly process. TFT array fabrication has the longest production cycle time. The processes in TFT array fabrication are similar to semiconductor manufacturing. The main stages of TFT array include cleaning, deposition, photolithography, etching, stripping and inspection with five to eight times re-entrant. The total manufacturing process required for a typical TFT array is about 30~50 steps. The manufacturing process of photolithography, including the processes of coating, exposure and developing, is usually the bottleneck among the entire array production processes. The complex constraints to be considered in the photolithography process, include the photo masks needed in the scanner machines, the constraints of available machines for each product in each layer, and the limited waiting time of work in process (WIP). The photo mask is the second resource of exposure process in additional to the scanner machines, and the exposure process cannot work without the usage of photo masks even though the scanner machines are available. Typically, every product in each layer requires a unique photo mask. Because the photo mask is expensive, one product generally has one set of masks only. Thus, the transportation time of photo masks between each machine also has to be considered. The available machines for each product in each layer are another constraint of photolithography process. Due to the quality issue, some of the photo masks are not allowed to use in some specific scanner machines. In turns, the products which require those photo masks will not allowed to process in those specific scanner machines. The available machines for each product is thus changed dynamically based on the condition and constraints of machines, and it will be updated daily. Moreover, if the WIP in the photolithography stage exceeds 24 hours, the products have to be reworked due to the quality consideration. In order to avoid rework, generally the factory will set a limited waiting time, which cannot be exceeded. The scheduling problem is therefore complex after considering the mentioned constraints. The utilization of photolithography depends highly on scheduling, by which the achievement of daily output target will also be impacted. In practice, the users have to maximize the utilization of photolithography stage with the consideration of complex constraints on one hand; they have to fulfill the daily output target of each product on the other hand. Therefore, most of the scheduling plan of TFT array manufacturing still highly depends on user’s experiences. The schedule plan will be scheduled and changed dynamically every day. Because of the highly uncertain schedule plan, it is difficult to forecast the daily throughput by an effective way. A multi-objective hybrid genetic algorithm (MO-HGA) to address the TFT-LCD module assembly scheduling problem by simultaneously considering the objectives, such as makespan, the weighted number of tardy jobs, and the total machine setup time [6]. Indeed, the proposed DPS integrated a GA-based scheduling system to maximize the utilization of photolithography stage under complex constraints and the simulation-based scheduling system to maximize the daily output target fulfillment of TFT array factory. By the results of scheduling, suggestions of scheduling and dispatching decisions can be made by the system. It follows the concept of industry 3.5 to make valuable improvement in the existing manufacturing system as listed in Table 2. Table 2. The comparisons between original factory and implementation of DPS system Features
Original factory
Implementation of DPS system
Automatic Level
Scheduled by the users; artificially.
Scheduled by the system; automatically.
Efficiency
Difficult and slow to schedule the plan
Easy and fast with the automatic computing
Flexibility
Complicated to implement new process, new
Flexible and adaptive by the usage of simulation-based
dispatching rules, and products
scheduling system
Optimal Methodology
Without optimal methodology
Genetic algorithms
Forecasting Mechanism
Without a system to forecast the throughput
Provide by the simulation-based scheduling system
2013
2014
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3.2. Approach In the present DPS system, the data including the daily release plan (daily input), the WIP condition in each machines of each stage, and the technical data are all collected form the Manufacturing execution system (MES) of the company, with the following information: 1. The takt time for each product in each layer and each stage. 2. The transportation time of each stage. 3. The manufacturing process of each product. 4. The photo mask allocation, and the available machines for each photo mask. The DPS system will update the above information daily. In order to generate the real time schedule plan, the WIP condition and daily planning schedule provided the latest information of current status of TFT array factory. A schedule plan, which aims to maximize the utilization of photolithography stage, will be generated by the GA-based scheduling system. The results will be implemented into the simulation-based scheduling system. Different types of product, different manufacturing processes of different products, the difference in process time between among layers and stages, as well as the transportation time among stages will be considered in the simulation-based scheduling system. Different dispatching rules will be designed. The schedule of photolithography and the schedule of TFT array factory will be shown by these two systems. Some of the graphical data will be generated in order to help the manager to understand the situation easily. The performance analyzer is based on the difference of the throughput and output of each product to calculate the performance of each experiment. Eventually, the manager can make scheduling decisions with the help of the system. The GA-based scheduling is implemented using Java program. The simulation-based scheduling system is developed using the simulation software Rockwell Arena 14.0. The loading schedule display system and the performance analyzer are built under the environment of Microsoft Excel 2013 and Microsoft Visual Basic for Application. The architecture of the DPS system is shown as Figure 2.
Fig. 2. The architecture of the DPS system
The target of the GA-based scheduling system is to maximize the utilization of the photolithography process because it is the bottleneck of TFT array manufacturing. Among the system, some constraints will be considered as shown in Table 3. A job is defined as a batch of specific product at a layer, which will be processed in the photolithography stage. The design of the chromosome consists of job sequence part and machine assignment part. The random key sequencing is used in each part, and each gene represents each job in both parts. In the job sequence parts, a random number between zero and one will be generated for each gene, which represents the process sequence of jobs in the same machine. The gene with the less number will be processed firstly. In the machine assignments parts, a random number between zero and one will be also generated. For each job, the available machine will be predetermined. The probability of job assignment to each available machine will be calculated as the roulette wheel, in which every machine represents an interval between zero and one. If the random number of gene is in the interval of specific machine, the job will be processed in that machine. For the first population, one hundred chromosomes will be generated.
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To generate new offspring, the first population will be sorted by the objective value and the best top fifty chromosomes will be picked up in order to create another fifty offspring chromosomes. The methods of crossover and mutation are used. For crossover, two of chromosomes will be paired randomly, and the one-cut point crossover and two-cut point mutation are used to generate offspring. The probability of crossover and mutation will be determined in advance. The fifty superior chromosomes of initial population and the fifty offspring generated by crossover and mutation will be population of next generation. The system will evolve continuously until one thousand generations. The best solution will be shown after the system terminated. Table 3. Constraints considered in the proposed GA-based scheduling system Considered Constraints
Explanation
Machine capacity constraints
One job can only be processed in one machine at the same time.
Job arrival time constraints
A job can be processed only if it the job has arrived. The arrival time of each job is
Photo mask constraints
The job can be processed only if the photo mask of current job is in the machine.
predetermined. Photo mask transportation time constraints
The transportation time of photo mask between each machine has to be considered. The photo masks can be used only if the photo mask is transported to specified machines. The transportation time between each machine is determined in advance.
Available machines constraints
The jobs can be processed only in the specified machines. The available machines for each job are predetermined.
Limited waiting time constraints
The waiting time of the jobs cannot exceed the limited waiting time. Otherwise a penalty will be implemented in the objective function. It is a soft constraint.
Then, the developed simulation-based scheduling system is employed to generate the scheduling plan of entire TFT array factory to maximize daily output fulfillment and fulfill the daily output target. Meanwhile, the design of the system allows to change the configuration easily to strengthen the flexible of the system. The construction of the system includes the entire TFT array manufacturing steps. Among them, the schedule plan of photolithography is implemented from the GA-based scheduling system. A job can be defined as a batch of specific product at a layer, which will be processed in the TFT array factory. The job will read the output files of GA-based scheduling system and reappear the schedule plan once it enters the photolithography stage. For other stages, the available machines for each job will be defined in advance. The job will be assigned to the available machine, in which the WIP is minimal. After the manufacturing process in the current stage finished, the job will be transported to the next stage by the predetermined manufacturing sequence until the last stage of current job. The job information including the location of current stage, the entering time of current stage, the product name, the lot size of current job and the processed machine will be recorded. The output data will input into the performance analyzer to generate the performance index. The scheduling results and the graphical data will be shown after the simulation completed. The result of GAbased scheduling system can be shown as a Gantt chart to represent the scheduling sequences of jobs in each machine of photolithography stage. Table 4 listed the improvement of the proposed GA-based scheduling system than existing approaches. For the part of simulation-based scheduling system, the predetermined dispatching rules will be experimented. Among the rules, the users also allow to define the priority of the products to accelerate the manufacturing progress of important products. The performance analyzer will analyze the scheduling results based on the throughput and the target of each product, to find a dispatching rule which maximize the output target fulfillment. A graph will be generated for each product. The throughput, the WIP condition for each step, and the difference between the throughput and output target in the end of simulation are shown. Through the above information, the users are able to understand the progress of each product. Furthermore, the output target for next three days and the accumulated value of WIP from the last step to first step are indicated. It shows the expected output amount of current WIP for next three days, and whether the current WIP is on schedule to fulfill the target of next three days. Thus, the users can make the corresponding response in advance to reach the target. A suggestion of
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priority setting of each product for next day will be generated, to accelerate the delayed products and fulfill the output target next day. Table 4. Improvement of GA-based scheduling system Performance Index
Original factory
Implementation of DPS system
Utilization of Photolithography
98%
99%
Time Required
More than 30 minutes
Less than 5 minutes
Reschedule Required
Yes
No
4. Conclusion Facing the challenges of national manufacturing strategies such as Industry 4.0 of Germany [7], Advanced Manufacturing Partnership (AMP) of USA [8], Industry 4.1J of Japan, and Made in China 2025 [9], Taiwanese companies need to find the right strategy to enhance the core competence and upgrade industry structure. However, it is unrealistic to apply the same strategies to Taiwan in the short-term since the background of the industries and the infrastructures are different. Driven by the enabling technologies and their innovative applications, this study aims to propose possible directions to support Taiwanese industries to find niche positions in the restructuring value chain and conduct an empirical study in a leading TFT-LCD company as illustration of smart production. With the integration of previous experiences and the present study, “Industry 3.5” is proposed in this study as the hybrid strategy of Industry 3.0 and Industry 4.0, to empower the manufacturing intelligence in the existing manufacturing environments of Taiwan. The DPS system of TFT array manufacturing is proposed in this study. The GA-based scheduling system and the simulation-based system are developed to maximize the utilization of photolithography stage and fulfill the daily output target of each product in the TFT array factory. This study focuses on the integration of domain knowledge and decision rules derived from big data analysis and simulation to find effective directions to improve performance of manufacturing, and achieve the decision making ability of smart factory. The results have shown practical viability of the developed solutions to enhance the productivity and flexibility. Indeed, it has been implemented in the case company. Further research can be done in various industries for upgrading existing factories and enhancing the digital decision capability of dispatching and scheduling for smart production. Acknowledgements This research is supported by Ministry of Science and Technology (MOST105-2218-E-007-028; MOST 1052218-E-007-027) and the Toward World Class University Project from Ministry of Education (105N536CE1), Taiwan. References [1] C.-F. Chien, K.-Y. Lin, J.-B. Sheu, C.-H. Wu. Retrospect and Prospect on Operations and Management Journals in Taiwan: From Industry 3.0 to Industry 3.5. J Management 33 (1)(2016) 87-103. [2] C.-F. Chien, Y. J. Chen, and J. T. Peng. Manufacturing intelligence for semiconductor demand forecast based on technology diffusion and product life cycle. IJPE 128 (2)(2010) 496-509. [3] C.-F. Chien, S. C. Chuang. A framework for root cause detection of sub-batch processing system for semiconductor manufacturing big data analytics, IEEE Trans. Semiconductor Manufacturing 27 (4)(2014) 475-488. [4] C.-F. Chien, Y.J. Chen, C.Y. Hsu, H.K. Wang. Overlay error compensation using advanced process control with dynamically adjusted proportional-integral R2R controller. IEEE Trans. Automation Science and Engineering 11 (2)(2014) 473-484. [5] C.-F. Chien, Y. J. Chen, and C. Y. Hsu. A novel approach to hedge and compensate the critical dimension variation of the developed-andetched circuit patterns for yield enhancement in semiconductor manufacturing. Computers & Operations Research 53(2015) 309-318. [6] C.-W. Chou, C.-F. Chien, M. Gen. A multiobjective hybrid genetic algorithm for TFT-LCD module assembly scheduling. IEEE Trans. Automation Science and Engineering 11 (3)(2014) 692-705. [7] Federal Ministry of Education and Research. Recommendation the Strategic Initiative INDUSTRIE 4.0. National Academy of Science and Engineering of Germany (2013).
Chen-Fu Chien et al. / Procedia Manufacturing 11 (2017) 2009 – 2017 [8] Executive Office of the President of the United States. Accelerating U.S. Advanced Manufacturing, AMP2.0 Steering Committee Report. The President.S. Advanced Manufacturing, AMP2.0 Steeringology of the United States (2014). [9] State Council of China. China Manufacturing 2025. (2015), available: http://www.gov.cn/zhengce/content/2015-05/19/content_9784.htm, last accessed: July, 7th, 2015.
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