IMPROVEMENT IN PRODUCTIVITY USING KANBAN PROCESS BASED COGNITIVE AUTOMATION SYSTEM

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1 International Journal of Mechanical Engineering and Technology (IJMET) Volume 9, Issue 5, May 2018, pp , Article ID: IJMET_09_05_015 Available online at ISSN Print: and ISSN Online: IAEME Publication Scopus Indexed IMPROVEMENT IN PRODUCTIVITY USING KANBAN PROCESS BASED COGNITIVE AUTOMATION SYSTEM Bikash Panigrahi Mechanical Engineering Department, Indira Gandhi Institute of Technology, Sarang, Odisha, India Dhiren Kumar Behera* Mechanical Engineering Department, Indira Gandhi Institute of Technology, Sarang, Odisha, India *Corresponding author ABSTRACT The aim of this study is to study the implementation of industrial engineering tools in selected manufacturing company to identify the highest defects occurred at the company production lines and propose new methods to the selected manufacturing company for defects reduction and thus improve the productivity of the company. The chosen company is VISA Steel Industries located at Jajpur, Odisha. In this research work all the work is performed by the help of the MATLAB R13(SIMULINK) version and we have consider flows of material as well as flows of kanbans. The many models given in the literature contribute to the confusion and debate that often characterize kanban research. The only element common to all kanban systems appears to be finite buffer capacities. We describe blocking by total queue size, blocking by part type, and kanban card systems. It review the kanban literature and organize it by type of system and decision area. First, we discuss elements of system design, including setting kanban numbers, performance measures, material-handling frequencies, and container sizes. Then we cover the production control topics of sequencing and sending it to assembly line. Finally kanban methods has a greater production control and is a very good system for production improvement for industries. Keywords: Kanban, Production, MATLAB/SIMULINK Cite this Article: Bikash Panigrahi and Dhiren Kumar Behera, Improvement in Productivity Using Kanban Process Based Cognitive Automation System, International Journal of Mechanical Engineering and Technology, 9(5), 2018, pp editor@iaeme.com

2 Improvement in Productivity Using Kanban Process Based Cognitive Automation System 1. INTRODUCTION In this modern and competitive world, manufacturing industry is one of the sectors, which can takes turns under all types of economic systems such as free market economy and collectivist economy. All of the products generated are competing to gain demand and satisfactory from customers based on quality and JIT concepts. Kanban is a system to control the logistical chain from a production point of view[1], and is an inventory control system. Kanban became an effective tool to support running a production system as a whole, and an excellent way to promote improvement.[2] Figure 1 Kannban System Data for the selected assembly line factory are collected, studied and analyzed. The defect with the highest frequency will be the main target to be improved. Various causes of the defect will be analyzed and various solving method exist. The best solving method will be chosen and propose to the company and compare to the previous result or production. However, the implementation of the solving methods is depending on the company whether they wanted to apply or not. 2. LITERATURE REVIEW Popovic, D. etal. (2018) in their paper" Off-site manufacturing systems development in timber house building: Towards mass customization-oriented manufacturing" described the need for housing in Sweden has been displaying a regular growth over the past couple of years. However, this situation might alternate in 2020 when you consider that there are warning signs that the increase in demand will reach its top. On the other hand, the usage of wooden as a load bearing structure has turn out to be more popular within the multi-family house building sector. It is competing with concrete and metallic frames, and its market share might even attain 50% through the year of Pramanik, P. K. etal. (2018) in their paper "Beyond Automation: The Cognitive IoT. Artificial Intelligence Brings Sense to the Internet of Things" proposed the Internet of Things (IoT) has already been infiltrated to our normal life in the kinds of smartphones, smart TVs, fitness trackers, health monitoring structures, clever watches, vending machines, clever meters, town traffic, building security structures and lots extra like this. It has advised the automation to a brand new excessive era. But IoT has confined functionality. To gain the real gain of IoT, it needs to be shrewd. In this bankruptcy, we've got reasoned why IoT wishes Artificial Intelligence (AI). The intelligent IoT that tell us here, is termed as the Cognitive IoT (CIoT) editor@iaeme.com

3 Bikash Panigrahi and Dhiren Kumar Behera Åkerman, M., et.al (2016).in their paper "Introducing customized ICT for operators in manufacturing." demonstrated higher complexity and accelerated global opposition emphasizes the want of pliability for corporations in production. To acquire this puts demanding situations on today's production systems and the human operators doing the obligations of that. 3. FRAMEWORK Figure 2 kanban based production system Figure 2 shows that the case study and a computer simulation with the help of the MATLAB R13 of a kanban based material handling system in a particular company (visa steel). 4. PROPOSED METHOD A case study The chosen company is VISA Steel Industries. In the research work, I have consider flows of material as well as flows of Kanban. In VISA steel, I have completed my project on improvement of production using KANBAN system. In KANBAN, the signal for material replenishment can be triggered, for example, by the work center that requires the material (demand source) by sending a card to the work center that is responsible for manufacturing the material (supply source). This card describes which material is required, the quantity of the material required and where the material is to be delivered. The name KANBAN originally stems from these cards, which are called "Kanban" in Japanese. Objective Eliminate disruptions Make system flexible by reduce setup and lead times Eliminate waste, especially excess inventory Make the process smooth editor@iaeme.com

4 Improvement in Productivity Using Kanban Process Based Cognitive Automation System Cognitive automation Cognitive automation is based on software bringing intelligence to information-intensive processes. It is commonly associated with Robotic Process Automation (RPA)[3] as the conjunction between Artificial Intelligence (AI) and Cognitive Computing[4]. By leveraging Artificial Intelligence technologies, cognitive automation extends and improves the range of actions that are typically correlated with RPA, providing advantages for cost savings[5] and customer satisfaction as well as more benefits in terms of accuracy in complex business processes that involve the use of unstructured information. Cognitive automation is not machine learning. Cognitive automation leverages different algorithms and technology approaches such as natural language processing [6], text analytics and data mining, semantic technology and machine learning. Industrial automation is the use of control systems, such as computers or robots, and information technologies for handling different processes and machineries in an industry to replace a human being. It is the second step beyond mechanization in the scope of industrialization.[7,8,9] Increase Quality and Flexibility in Manufacturing Process Earlier the purpose of automation was to increase productivity (since automated systems can work 24 hours a day), and to reduce the cost associated with human operators (i.e. wages & benefits)[10,11]. However, today, the focus of automation has shifted to increasing quality and flexibility in a manufacturing process. In the automobile industry, the installation of pistons into the engine used to be performed manually with an error rate of 1-1.5%. Presently, this task is performed using automated machinery with an error rate of %.[2] Overview This model simulates a production system that uses kanbans to manage production activities. Analysis of simulation results highlights problems of the system and suggests ways to improve its performance. Formula for determining the number of cards used in the system Below is presented a formula which is using in production to show how kanbans could low between a customer cell and a supplier cell.[10,12] The modeled production system includes two part suppliers and an assembly line. The part suppliers use raw materials to manufacture parts. Finished parts are transported to the assembly line to fabricate final products. Completed products are shipped to distributors to fill production orders. At the top level of the model: The Generate Production Orders subsystem simulates the generation of production orders. The Assembly Line subsystem fills a production order by assembling two types of parts (referred to as part A and part B) into final products editor@iaeme.com

5 Bikash Panigrahi and Dhiren Kumar Behera The Part A Supplier subsystem and Part B Supplier subsystem manufacture the parts needed for final assembly. The Material A Supplier subsystem and Material B Supplier subsystem replenish the raw materials consumed during parts production. Figure 2 Kannban System MATLAB/SIMULINK Model The model uses a preloaded queue technique to model the group of kanbans. To To learn about the Kanban request and release technique in this model has been adopter Resource Allocation from Multiple Pools can be further used editor@iaeme.com

6 Improvement in Productivity Using Kanban Process Based Cognitive Automation System 5. SIMULATION RESULT Results and Displays During simulation, the Data Display subsystem displays these scopes to demonstrate the performance of the production system: PartA Withdrawal Kanban Backlog PartB Withdrawal Kanban Backlog Number of Part A in Process Number of Part B in Process Number of Products in Final Assembly Number of Part A in Storage Number of Part B in Storage Product Demand Number of Dropped Orders Number of Completed Orders A Display block at the root level of the model provides a numeric view of the number of orders completed as well as the number of orders dropped. Using the Model for Performance Analysis The model with the original configuration represents a Kanban production system with significant lost sales in months when demand is at a peak. Analysis of simulation results suggests solutions to address this issue. The following graph shows how the solutions are developed. Results Display Figure 3 Figure editor@iaeme.com

7 Bikash Panigrahi and Dhiren Kumar Behera Figure 5 Figure 6 Figure 7 Figure 8 Figure editor@iaeme.com

8 Improvement in Productivity Using Kanban Process Based Cognitive Automation System Figure CONCLUSION In the proposed work, we have consider flows of material as well as flows of kanbans. The many models given in the literature contribute to the confusion and debate that often characterize Kanban research. The only element common to all Kanban systems appears to be finite buffer capacities. we describe blocking by total queue size, blocking by part type, and Kanban card systems. We review the Kanban literature and organize it by type of system and decision area and finally conclude a Kanban method are a greater production control and is a very good system for production improvement for industries. REFERENCES [1] Choe, P., Tew, J. D., & Tong, S. (2015). Effect of cognitive automation in a material handling system on manufacturing flexibility. International Journal of Production Economics, 170, [2] Sugimori, Y., Kusunoki, K., Cho, F., & Uchikawa, S. (1977). Toyota production system and kanban system materialization of just-in-time and respect-for-human system. The International Journal of Production Research, 15(6), [3] Pramanik, P. K. (2018). Beyond Automation: The Cognitive IoT. Artificial Intelligence Brings Sense to the Internet of Things. [4] Popovic, D. (2018). Off-site manufacturing systems development in timber house building: Towards mass customization-oriented manufacturing (Doctoral dissertation, Jönköping University, School of Engineering). [5] Fette, M., (2016). Automated and Cost-efficient Production of Hybrid Sheet Moulding Compound Aircraft Components. [6] Brand, Y. (2018, January). Design and Experimental Validation of Transparent Behavior for a Workload-Adaptive Cognitive Agent. [7] Patil Sanjay and Hukari Nand Kumar, Industrial Engineering and Production and Operations Management, fourth Edition, ElectroTech Publication, Satara, 2007, PP. 236.International Labour Organisation, Introduction to Work Study, Universal Publishing Corporation, India, 1986, PP.4. [8] Stevenson William J, Production and Operations Management, Boston, MA: Irwin McGraw-Hill, 1999.International Labour Organisation, Ibid, P.4. [9] Jhamb L.C., Production (Operations) Management, Everest Publishing House Pune, th Edition, PP [10] Fasth,Å.,Mattsson,S.,Fassberg,T.,Stahre,J.,Hoog,S.,Sterner,M.,Andersson,T.,2011.Develo pment of production cells with regard to physical and cognitive automation: a decade of evolution. In Proceedings of the Assembly and Manufacturing (ISAM),2011IEEE International Symposiumon.IEEE, editor@iaeme.com

9 Bikash Panigrahi and Dhiren Kumar Behera [11] Chester L.Brisley, Work Measurement in the 1980 s, 43rd Annual IMS Clinic Proceedings, Industrial Management Society, Des Plaines, IL. [12] Berkley, B. J. (1992). A review of the kanban production control research literature. Production and operations management, 1(4), [13] Wang, L., Chen, X., & Liu, Q. (2017). A Lightweight Intelligent Manufacturing System Based on Cloud [14] Fette, M., (2016). Automated and Cost-efficient Production of Hybrid Sheet Moulding Compound Aircraft Components. [15] A. Suresh and Dr. N. Somasundaram, A Study on Impact of Barcode and Radio Frequency Identification Technology on Maximized Productivity in Manufacturing Industries at Sipcot, Chennai. International Journal of Management, 8(1), 2017, pp [16] G. Indhu mathi and M. Thirumakkal, A Study on Role of Occupational Stress on Employees Productivity, International Journal of Management (IJM), Volume 6, Issue 1, January (2015), pp [17] Sulaiman, Eko Julianto Sasono, Sulistiyono Susilo, Suharto, Factors Affecting Shipbuilding Productivity, International Journal of Civil Engineering and Technology (IJCIET) Volume 8, Issue 7, July 2017, pp editor@iaeme.com