Impact of Just in Time in Manufacturing

Impact of Just in Time in ManufacturingCHAPTER IINTRODUCTIONIntroduction to the ProblemStatement of the Problem It had been proved from magazine and again the positive impact of Just in Time in manufacturing. No models or methodologies have been developed to relate how predictive maintenance posterior have a significant effect on the procedure of JIT in manufacturing and its supply chains.In aboriginal 1950s Toyota devised their manufacturing system called Toyota business system which stream keys the immaculate process of manufacturing in an organized way through continuous information sharing between supplier and customer to achieve just- in- time production. Just-in-time is one of the major(ip) pillars of Toyota production system. Implementation of lean principles gave way for various strategic advantages in manufacturing. (Lathin, 2001) stated that using lean principles, a traditional mass producer could face a reduction of 90% in inventory, cost in quality, lead time and 50% increase in labor productivity. (Nystuen, 2002) stated that one could see a reduction of 90% in travel time, 82% in inventory and 11% in product lead time. After the success of Toyota production system, although this production system changeized the entire process 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), deficiency of machine capability, high inventory, fluctuating markets (Golhar, Stamm, and Smith, 1990), high product variety (Cusumano, 1994) and miss of communication between processes. One of the biggest key suspects understands machine capability. This can be achieved by filling the gap between machine capability information and production planning. To achieve this system, thither are 2 key elements Real- time machine information and data processing. P.ODonovan, K. Leahy, K. Bruton and D.T.J. OSullivan (2015) presented a concept called smart manufacturing where manu facturing data can be used create positive impact on the manufacturing operationsThe first industrial revolution began in early 1800s through mechanical production using steam and water. Since then, there has been two other industrial revolutions through assembly line production for mass production, increase quality, reduce cost and manufacturing time and using engine room and IT systems. Currently the manufacturing perseverance 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 become smarter and are capable of collecting their work as feedback. It has always been a challenge to fix the machines during downtime and machines technicians are also required to keep 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 analyzed using predictive tools for machine predictability. Connecting the machines through cyberspace enables managers to monitor every machines performance remotely without visiting every machine during the day.Significance of the ResearchResearch Questions and ObjectivesImplementation of predictive maintenance has been a buzzword for some time in Internet of Things (IoT) neighborhood. In the recent categorys, many companies have been implementing predictive maintenance activities it to their advantage in order to achieve machine failure guiltless environment. there has been a lot of case studies published in the recent times on capital punishment of predictive maintenance activities with results closer to machine failure free operation. Most of research in predictive maintenance in recent times have focused on different methodologies and algorithms implemented in data digging, classification and prediction in order to achieve failure free operation. In the course of literature review it was found that, there has been a lack of research in studying the effect of implementation of predictive maintenance activities throughout manufacturing supply 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 mind the effect on the performance of Just-in-time in manufacturing before the implementation of predictive mai ntenance activity?This research study is conducted to answer these questions by collecting and exploit 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 measuring performance variance in Just-in-time for an industry environment and throughout its supply chains by implementing prophetical maintenance activity.The identification of Just-in-time performance measurement factors that would have significant effect in predicting the performance before implementation of predictive maintenance activityThe creation, hindrance and validation of a model that could estimate the performance variance in Just-in-time for future implementations throughout the supply chainCHAPTER IILiterature reviewOverviewJIT in ManufacturingMachine victualsAll actions appropriate for re taining an item/ branch/equipment in, or restoring it to, a given condition 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). in that location are 2 types of machine maintenance and are classified as follows. plotted maintenance is generally classified as hold onive (PM) and corrective maintenance, date breakdown maintenance is considered as unplanned. Preventive maintenance can be further subdivided into fixed maintenance and predictive maintenance. (Mansor, Ohsato, Sulaiman, 2012).Unplanned DowntimeThe unscheduled 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 DowntimeTh ere 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 crossways different areas. One of the main objectives of PM is to keep the machine in running condition through standard inspection methods and correction methods at early deficiency 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). nearly 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).Fixed maintenancePredictive maintenance exchangeable to preventive mai ntenance, predictive maintenance have several definitions. To some workers, predictive maintenance is monitoring the vibration of rotating machinery in an attempt to detect incipient problems and to prevent catastrophic failure or it is monitoring the infrared image of electrical switchgear, motors or other electrical equipment to detect developing 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 unconventional 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 five nondestructive techniques used for predictive maintenance management vibration monitoring, process parameter monitoring, thermography, tribology, and visual inspection.Pr edictive maintenance not just trammel 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 Automotive manufacturer increases productivity for cylinder-head production by 25 percentIBM summation Analytics for Manufacturing Equipment in AutomotiveIsrael Electric Corporation moves towards smarter maintenanceFluke Corporation White Paper ThermographyRoland Berger Oil and gas Reducing breakdowns and increasing production of highly critical assetsRoland Berger Wind Power Reducing maintenance costs and improving uptime in a challenging operational environmentABB Group Predictive Maintenance for Heavy IndustryData collection, Data mining and Predictive maintenance methodologiesData CollectionSensor dataHistorical dataData mining TechniquesSignal Processing and Feature ExtractionPrinciple Component Analysis (PCA) based fault detectionPredictive Maintenance methodologiesHealth AssessmentSelf-organizing chromosome mapping (SOM)Performance PredictionHealth DiagnosisSelf-organizing map (SOM)ReferencesLatino, C.J., Hidden Treasure Eliminating Chronic 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 2180-1606 (Online) Volume 5, pp. 612-621, January-June 2012Levitt, J., Managing prev entive maintenance, Maintenance technology, February 1997, 20-30.Mobley, R Keith, An Introduction to predictive maintenance, 2002, second ed, ISBN 0-7506-7531-4