Impact of Just in Time in Manufacturing

Impact of tho in Time in ManufacturingCHAPTER IINTRODUCTIONIntroduction to the botherStatement of the Problem It had been proved from measure and again the positive carry on of Just in Time in manufacturing. No sticks or methodologies squander been developed to relate how prognostic nourishment stooge have a significant effect on the executing of JIT in manufacturing and its make out chains.In early 1950s Toyota devised their manufacturing system called Toyota production system which streamlines the entire forge of manufacturing in an organized way through continuous information communion between supplier and customer to achieve just- in- time production. Just-in-time is one of the major pillars of Toyota production system. murder of lean principles gave way for various strategic advantages in manufacturing. (Lathin, 2001) give tongue to that exploitation lean principles, a traditional mass producer could expect a reduction of 90% in inventory, cost in quality, pa ss on time and 50% increase in labor productivity. (Nystuen, 2002) readd that one could see a reduction of 90% in get off time, 82% in inventory and 11% in product lead time. After the success of Toyota production system, although this production system renewingized the entire performance of manufacturing in Japan, it did not reflect the west. This is due to many reasons such as traditionally minded management (Gupta and Jain, 2013), lack of implement capability, high inventory, displace markets (Golhar, Stamm, and Smith, 1990), high product variety (Cusumano, 1994) and lack of communication between attendes. 1 of the biggest key suspects understands weapon capability. This can be achieved by filling the disruption between mold capability information and production planning. To achieve this system, there are 2 key elements Real- time machine entropy and selective information processing. P.ODonovan, K. Leahy, K. Bruton and D.T.J. OSullivan (2015) presented a concept cal led smart manufacturing where manufacturing data can be utilize create positive impact on the manufacturing operationsThe first industrial revolution began in early 1800s through mechanical production utilise steam and water. Since then, there has been two other industrial revolutions through fable line production for mass production, increase quality, reduce cost and manufacturing time and development technology and IT systems. Currently the manufacturing manufacturing is in the midst of data driven revolution transforming traditional manufacturing facilities into smart manufacturing facilities (Peter ODonovan, Kevin Leahy, Ken Bruton and Dominic T. J. OSullivan, 2015). Many industry Pundits today believe we are currently undergoing fourth industrial revolution through internet technology in manufacturing.Machine reliability has always played an important role for manufacturing. Over time machines have receive smarter and are capable of pile up their performance as feedbac k. It has always been a challenge to fix the machines during downtime and machines technicians are also required to concord themselves updated on latest technologies. (Jay Lee, Hung-An Kao, Shanhu Yang, 2014) suggested that machines could be connected together in a cyber-workspace where, machine data could be collected and later(prenominal) canvassd using prophetic tools for machine predictability. Connecting the machines through cyberspace enables managers to superintend every machines performance remotely without visiting every machine during the day.Significance of the ResearchResearch Questions and ObjectivesImplementation of prognostic upkeep has been a buzzword for round time in Internet of Things (IoT) neighborhood. In the upstart years, many companies have been implementing prognostic precaution activities it to their advantage in baffle to achieve machine failure at large(p) environment. thither has been a lot of case studies published in the recent times on performance of predictive sustentation activities with results closer to machine failure free operation. Most of seek in predictive tutelage in recent times have foc utilize on unlike methodologies and algorithms implemented in data mining, classification and prediction in order to achieve failure free operation. In the course of literature reexamine it was found that, there has been a lack of research in analyze the effect of implementation of predictive maintenance activities throughout manufacturing issue chains. This research study is conducted to answer some of the questions in an industry environment such as (1) What was effect in product flow by implementing predictive maintenance activities? (2) How were the supply chains impacted by the implementation of predictive maintenance activity (3) Was there any effect on the performance of Just-in-time manufacturing? (4) If so, what factors were affected and by how much? (5) Can a model musical theme the effect on the perform ance of Just-in-time in manufacturing before the implementation of predictive maintenance activity?This research study is conducted to answer these questions by collecting and mining data from current manufacturing setup and its supply chains, applying new methods to analyze it and use traditional regression models to predict the performance change in Just-in-time in manufacturing. The objectives in this research includesThe development of a methodology for quantity performance variance in Just-in-time for an industry environment and throughout its supply chains by implementing Predictive maintenance activity.The identification of Just-in-time performance quantity factors that would have significant effect in predicting the performance before implementation of predictive maintenance activityThe creation, verification and validation of a model that could estimate the performance variance in Just-in-time for future implementations throughout the supply chainCHAPTER IILiterature revi ewOverviewJIT in ManufacturingMachine chargeAll actions appropriate for retaining an dot/part/equipment in, or restoring it to, a accustomed embodiment is known as maintenance (Dhillion, 2002). Each year US manufacturing industry spends about $300 billion on plan maintenance and operations. It is also estimated that approximately 80% of the industry budget goes towards correcting chronic failures of machines, systems and peoples (Latino, 1999). There are 2 types of machine maintenance and are classified as follows. Planned maintenance is generally classified as contraceptive (PM) and disciplinary maintenance, while breakdown maintenance is considered as unplanned. Preventive maintenance can be further subdivided into fixed maintenance and predictive maintenance. (Mansor, Ohsato, Sulaiman, 2012).Unplanned DowntimeThe special maintenance or repair to return items/equipment to a defined state and carried out because maintenance persons or users perceived deficiencies or failures is known as corrective maintenance (Dhillion, 2002).Planned DowntimeThere are many definitions to preventive maintenance. All actions carried out on a planned, periodic, and specific schedule to keep an item/equipment in stated working condition through the process of checking and reconditioning is known as preventive maintenance (Dhillion, 2002). In the recent years, PM has been one of the most sought techniques in industries across different areas. nonpareil of the main objectives of PM is to keep the machine in running condition through standard inspection methods and correction methods at early want stages. Performing PM activities has some of the advantages such as increasing equipment availability, reduction of overtime, reduction in inventory, improve safety, improve quality, reduces time and cost (Levitt, 1997). rough of the disadvantages of PM are it increases initial cost, damaging equipment, reduces life of parts and using more number of newer parts (Patton, 1983).Fix ed maintenancePredictive maintenanceSimilar to preventive maintenance, predictive maintenance have several definitions. To some workers, predictive maintenance is monitoring the vibration of rotating machinery in an attempt to feel incipient problems and to prevent catastrophic failure or it is monitoring the infrared emission image of electrical switchgear, motors or other electrical equipment to detect maturation problem (Mobley, 2002). According to Dhillion (2002), predictive maintenance is a method of using modern measurement and signal processing methods to accurately diagnose item/ equipment condition during operation. It would not be wrong to say, Predictive maintenance is a complement of preventive maintenance which uses various testing and measuring methods to monitor the equipment status and predict the machine failures.According to Mobley (2002), there are phoebe bird nondestructive techniques used for predictive maintenance management vibration monitoring, process co ntention monitoring, thermography, tribology, and visual inspection.Predictive maintenance not just limited to manufacturing sectors used various other industry such as water and wastewater utility solutions (Severn Trent Services), Transportation railway (Finnish railway VR Group), Power grids (Israel Electric corporation), vegetable oil and gas industry, wind power (Roland Berger Strategy Consultants, 2014), Airline industry (IBM, 2014), Biotech industry (Cypress Envirosystems, 2008) and many more. Some of the case studies related to manufacturing would be discussed in later part of this report.Case StudiesKALYPSO Predictive analytics and Improved Product design with machine learningDaimler self-propelling manufacturer increases productivity for cylinder-head production by 25 percentIBM Asset Analytics for Manufacturing Equipment in AutomotiveIsrael Electric Corporation moves towards smarter maintenanceFluke Corporation White Paper ThermographyRoland Berger Oil and gas decrea se breakdowns and increasing production of highly critical assetsRoland Berger Wind Power reduce maintenance costs and improving uptime in a challenging running(a) environmentABB Group Predictive Maintenance for Heavy Industry data collection, Data mining and Predictive maintenance methodologiesData Collection sensor dataHistorical dataData mining TechniquesSignal affect and Feature ExtractionPrinciple Component Analysis (PCA) based misunderstanding detectionPredictive Maintenance methodologiesHealth AssessmentSelf-organizing map (SOM) surgical operation PredictionHealth DiagnosisSelf-organizing map (SOM)ReferencesLatino, C.J., Hidden Treasure Eliminating inveterate Failures Can Cut Maintenance Costs up to 60%, Report, dependability Center, Hopewell, Virginia, 1999M.A. Mansor, A. Ohsato and S. Sulaiman, KNOWLEDGE MANAGEMENT FOR MAINTENANCE ACTIVITIES IN THE MANUFACTURING SECTOR, International Journal of Automotive and Mechanical Engineering, SSN 2229-8649 (Print) ISSN 2180-160 6 (Online) Volume 5, pp. 612-621, January-June 2012Levitt, J., Managing preventive maintenance, Maintenance technology, February 1997, 20-30.Mobley, R Keith, An Introduction to predictive maintenance, 2002, 2nd ed, ISBN 0-7506-7531-4