Development Of Overall Performance Effectiveness In Job Shop Manufacturing Process

Overall equipment effectiveness (OEE) is implemented by the case company,an aerospace part manufacturing company, to encourage machines to operate all the time at the ideal speed and produce no quality defect in extreme case.However,integration between workstations and transporting activities,deviation of production from customer demand,and imbalanced capacity among processes are neglected under OEE implementation.The consequences include inefficient material flow,overproduction and excessive inventory level,as well as lack of interaction between workstations.Therefore,objectives of this study aim to quantify the impact of transportation efficiency onto the workstations,to synchronize capacity available among them and also to monitor the fulfillment of customer demand in terms of delivery time and production amount.The critical measures are shorter lead time and wait time,less throughput,minimal equipment utilization and less capacity incurred.Simulation results have shown that both transportation efficiency and performance of Autoclave workstation affect material flow and throughput rate respectively.Consequently,the performance of workstations they connect with are also affected.Besides, simulation also proves different production rate and imbalanced capacity throughout production system. Therefore,Overall Performance Effectiveness (OPE) is proposed to consider customer demand,historical equipment utilization and Takt time of each workstation.This promotes reasonable utilization of resource to avoid both overprocessing and overproduction issues which are invisible in OEE.Furthermore,delay propagation throughout production system and interrelationship between processes are quantified by delivery performance (DP) of OPE.The waiting time and lead time spent in each workstation are monitored under the DP.Responsibility of all workstations and transportation process in delivering demand on time are quantified.Last but not least,transportation process which serves as the connectors of manufacturing processeses is also quantified and monitored by proposed Transportation Measure (TM).TM aims to reduce the queue length at destination and the corresponding waiting time with reasonable utilization of forklift.It also promotes less capacity investment in transportation and prioritizes its scheduling according to urgency of destination workstation.In short,newly proposed Overall Performance Effectiveness (OPE) and the quantification of Transportation Measure (TM),which affect each other,help in promoting better delivery performance in terms of production amount and lead time.Besides,reasonable utilization equipment and minimal consumption of material are promoted to fulfill the demand.The effectiveness of entire production line is examined as a unity with joint responsibility under varying transportation efficiency and cycle time of each workstation.Both OPE and TM could be implemented together to optimize the production system.All of these are not quantified and provided by the OEE implemented by the case company

Customer demand and planning efficiency in overall equipment effectiveness (OEE)

Overall equipment effectiveness (OEE) is the multiplication of availability, performance and quality which are looking into the losses such as downtime losses, speed losses and quality losses respectively. The study is carried out in an aerospace parts manufacturing company to acquire the time data of Autoclave section via the computerized recording system. In this study, planning factor is introduced in OEE and defined as the panel number loaded over the maximum number of panel affordable by autoclaves. This is to promote the optimization of Autoclave usage or the concept of On Time In Full (OTIF). In addition to that, customer demand is also incorporated in OEE through Takt Time. This evaluates Autoclaves with constant and fixed cycle time in varying performance ratio from time to time according to its real performance. This also provides a balance between the over-production issue and high utilization of equipment. The results of planning factor and Takt time incorporation show that curing of Autoclave was not planned up to its maximum capability; and the demand accepted is actually higher than its maximum capacity. After that, the OEE data once obtained is then used for versatile usages such as scheduling or planning of preventive maintenance frequency, examination of customer demand accepted with respect to Autoclave capacity, comparison of performance over the time horizon as well as the evaluation of material losses due to planning of equipment process. Among them the estimation ofbreakdown time (via MTBF) and priority of preventive maintenance on Autoclave is done by analyzing the availability data and the percentage of downtime composition. Another possible usage of OEE data is the calculation of material amount based on the Bill of Material (BOM) and the average of historical quality ratio data. As a conclusion, objectives are achieved since the customer demand is incorporated into OEE,efficiency of planning is examined and versatile usage of OEE value and data are demonstrated. Further study could be suggested is the possibility of implementing Takt time and planning factor in the equipment with varying cycle tim

Novel availability and performance ratio for internal transportation and manufacturing processes in job shop company

Purpose: Purpose of this study includes the quantification of the impact of transportation efficiency onto the workstations the transportation serves in term of throughput and total lead time elapsed by product. Besides, it aims to synchronize the capacity available among workstations throughout a production line by studying the upper limit of throughput could be afforded by each workstation as well as their connection with each other. This study is also done on the purpose of promoting fulfillment of customer demand at shorter delivery time and minimal equipment utilization. Investigation on implementation of Overall Equipment Effectiveness (OEE) in an aerospace part-manufacturing company is studied to track out the potential opportunities to be improved. Design/methodology/approach: Site observation is conducted on all the five manufacturing workstations in the aforementioned aerospace part manufacturing company. Time data of both automated processes and manual processes are collected and they are used to construct simulation model. From that, various scenarios of transportation efficiency are simulated in Experiment 1. In addition, Experiment 2 is also set to examine the maximum capacity of each workstation. All of these are to highlight the relationship between workstation and processes and to verify the condition of imbalanced capacity among workstations in the company. In short, this has necessitated the integration of workstation and transportation activities within the company. These are followed by proposal of measures to quantify the wastes identified. Findings: The paper finds that implementation of OEE alone does not consider the reasonability of customer demand fulfillment. The results show that both transportation efficiency and imbalanced capacity throughout production system are not emphasized by OEE implementation in the case company. Therefore, responsibility of all workstations and transportation process in delivering demand on time are quantified. Transportation process which serves as the connectors of manufacturing processes is quantified and monitored by proposed Transportation Measure (TM) whereas workstations are measured using novel availability and performance ratio. Research limitations/implications: Future research should be conducted to examine the impact of other station within a company such as warehouse and logistic department to the performance of equipment and materials in manufacturing workstation. Besides, the material availability as well as the skills or performance of man power could be further incorporated into the measures to consider all the entities involved in manufacturing processes. Practical implications: The proposed availability and performance ratio for both transportation and manufacturing processes, which are related to each other, help in promoting better effectiveness of production system in terms of production amount and lead time. Besides, reasonable utilization equipment and minimal consumption of material are incorporated in the measures to promote Lean way in fulfilling customer demand. The effectiveness of entire production line is examined as a unity with joint responsibility under varying transportation efficiency and cycle time of each workstation. Both measures could be implemented together to optimize the production system and quantify the hidden wastes which are neglected in the OEE implementation. Originality/value: The novel availability and performance ratio are proposed to consider customer demand, historical equipment utilization and Takt time of each workstation to examine the possibility and reasonability of demand fulfillment. This prevents both over-processing and overproduction issues which are invisible in OEE. Furthermore, delay propagation throughout production system and interrelationship between processes are quantified under transportation measure. Other novelty of the paper is that it monitors the waiting time and lead time spent in each workstation at the same time considering utilization of workstation. The proposed Transportation Measure (TM) aims to reduce the queue length and waiting time at destination workstation at minimal utilization of forklift. It also promotes less capacity investment in transportation and prioritizes its scheduling according to urgency of destination workstation.Peer Reviewe

Examination of Overall Equipment Effectiveness (OEE) in Term of Maynard's Operation Sequence Technique (MOST)

It is a common practice to quantify any process or entire production line in manufacturing industry especially to measure three main losses named time losses, performance losses and quality defect exist in production. Overall Equipment Effectiveness (OEE) fulfils the requirement by providing the measure of equipment via single measure which is monitored from time to time by responsible personnel so that corresponding optimization or Kaizen could be done. However, there are many lean wastes which could be 'invisible' or tolerated under the conventional definition of OEE. The hidden waste includes unnecessary production which was classified as operating time and the underestimated effect of excessive transportation or setup time. These could be minimized and sometimes avoidable via work measurement, method study and study of the work, which are under the study of Maynard's Operation Sequence Technique (MOST). This paper intends to examine and quantify the hidden lean waste in OEE from the perspective of method and work of an operation with the integration of MOST study. Operations are analyzed in every single step and broken down into details of activities, which are then re-designed for minimal non-value added activity in operation based on the standard allowable. The OEE data after the study of work is computed and compared with the OEE before the MOST study. The comparison shows the improvement in term of OEE after the MOST study and this implies that the hidden waste inside OEE definition could be tracked out for a better effectiveness. Any reduction in the non-value added activities or downtime ensure larger room for more value added activities or uptime and therefore the availability of production. It is expected to provide a new insight in implementing OEE at a different way and stay beware of the assumptions in OEE to avoid any hidden waste

Principal component analysis-aided statistical process optimisation (PASPO) for process improvement in industrial refineries

Integrated refineries and industrial processing plant in the real-world always face management and design difficulties to keep the processing operation lean and green. These challenges highlight the essentiality to improving product quality and yield without compromising environmental aspects. For various process system engineering application, traditional optimisation methodologies (i.e., pure mix-integer non-linear programming) can yield very precise global optimum solutions. However, for plant-wide optimisation, the generated solutions by such methods highly rely on the accuracy of the constructed model and often require an enumerate amount of process changes to be implemented in the real world. This paper solves this issue by using a special formulation of correlation-based principal component analysis (PCA) and Design of Experiment (DoE) methodologies to serve as statistical process optimisation for industrial refineries. The contribution of this work is that it provides an efficient framework for plant-wide optimisation based on plant operational data while not compromising on environmental impacts. Fundamentally, PCA is used to prioritise statistically significant process variables based on their respective contribution scores. The variables with high contribution score are then optimised by the experiment-based optimisation methodology. By doing so, the number of experiments run for process optimisation and process changes can be reduced by efficient prioritisation. Process cycle assessment ensures that no negative environmental impact is caused by the optimisation result. As a proof of concept, this framework is implemented in a real oil re-refining plant. The overall product yield was improved by 55.25% while overall product quality improved by 20.6%. Global Warming Potential (GWP) and Acidification Potential (AP) improved by 90.89% and 3.42% respectively

Principal component analysis-aided statistical process optimisation (PASPO) for process improvement in industrial refineries

Integrated refineries and industrial processing plant in the real-world always face management and design difficulties to keep the processing operation lean and green. These challenges highlight the essentiality to improving product quality and yield without compromising environmental aspects. For various process system engineering application, traditional optimisation methodologies (i.e., pure mix-integer non-linear programming) can yield very precise global optimum solutions. However, for plant-wide optimisation, the generated solutions by such methods highly rely on the accuracy of the constructed model and often require an enumerate amount of process changes to be implemented in the real world. This paper solves this issue by using a special formulation of correlation-based principal component analysis (PCA) and Design of Experiment (DoE) methodologies to serve as statistical process optimisation for industrial refineries. The contribution of this work is that it provides an efficient framework for plant-wide optimisation based on plant operational data while not compromising on environmental impacts. Fundamentally, PCA is used to prioritise statistically significant process variables based on their respective contribution scores. The variables with high contribution score are then optimised by the experiment-based optimisation methodology. By doing so, the number of experiments run for process optimisation and process changes can be reduced by efficient prioritisation. Process cycle assessment ensures that no negative environmental impact is caused by the optimisation result. As a proof of concept, this framework is implemented in a real oil re-refining plant. The overall product yield was improved by 55.25% while overall product quality improved by 20.6%. Global Warming Potential (GWP) and Acidification Potential (AP) improved by 90.89% and 3.42% respectively

Novel availability and performance ratio for internal transportation and manufacturing processes in job shop company

Purpose: Purpose of this study includes the quantification of the impact of transportation efficiency onto the workstations the transportation serves in term of throughput and total lead time elapsed by product. Besides, it aims to synchronize the capacity available among workstations throughout a production line by studying the upper limit of throughput could be afforded by each workstation as well as their connection with each other. This study is also done on the purpose of promoting fulfillment of customer demand at shorter delivery time and minimal equipment utilization. Investigation on implementation of Overall Equipment Effectiveness (OEE) in an aerospace part-manufacturing company is studied to track out the potential opportunities to be improved. Design/methodology/approach: Site observation is conducted on all the five manufacturing workstations in the aforementioned aerospace part manufacturing company. Time data of both automated processes and manual processes are collected and they are used to construct simulation model. From that, various scenarios of transportation efficiency are simulated in Experiment 1. In addition, Experiment 2 is also set to examine the maximum capacity of each workstation. All of these are to highlight the relationship between workstation and processes and to verify the condition of imbalanced capacity among workstations in the company. In short, this has necessitated the integration of workstation and transportation activities within the company. These are followed by proposal of measures to quantify the wastes identified. Findings: The paper finds that implementation of OEE alone does not consider the reasonability of customer demand fulfillment. The results show that both transportation efficiency and imbalanced capacity throughout production system are not emphasized by OEE implementation in the case company. Therefore, responsibility of all workstations and transportation process in delivering demand on time are quantified. Transportation process which serves as the connectors of manufacturing processes is quantified and monitored by proposed Transportation Measure (TM) whereas workstations are measured using novel availability and performance ratio. Research limitations/implications: Future research should be conducted to examine the impact of other station within a company such as warehouse and logistic department to the performance of equipment and materials in manufacturing workstation. Besides, the material availability as well as the skills or performance of man power could be further incorporated into the measures to consider all the entities involved in manufacturing processes. Practical implications: The proposed availability and performance ratio for both transportation and manufacturing processes, which are related to each other, help in promoting better effectiveness of production system in terms of production amount and lead time. Besides, reasonable utilization equipment and minimal consumption of material are incorporated in the measures to promote Lean way in fulfilling customer demand. The effectiveness of entire production line is examined as a unity with joint responsibility under varying transportation efficiency and cycle time of each workstation. Both measures could be implemented together to optimize the production system and quantify the hidden wastes which are neglected in the OEE implementation. Originality/value: The novel availability and performance ratio are proposed to consider customer demand, historical equipment utilization and Takt time of each workstation to examine the possibility and reasonability of demand fulfillment. This prevents both over-processing and overproduction issues which are invisible in OEE. Furthermore, delay propagation throughout production system and interrelationship between processes are quantified under transportation measure. Other novelty of the paper is that it monitors the waiting time and lead time spent in each workstation at the same time considering utilization of workstation. The proposed Transportation Measure (TM) aims to reduce the queue length and waiting time at destination workstation at minimal utilization of forklift. It also promotes less capacity investment in transportation and prioritizes its scheduling according to urgency of destination workstation.Peer Reviewe