MANUFACTURING PRODUCTIVITY IMPROVEMENT BASED ON OVERALL LINE EFFECTIVENESS

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1 MANUFACTURING PRODUCTIVITY IMPROVEMENT BASED ON OVERALL LINE EFFECTIVENESS Rehab E. El badway Mechanical Engineering, NARSS, Egypt. A.M. Elhady Adjunct Prof. NExSat Project Manager, NARSS, Egypt. Maha M. A. Lashin Mechanical Engineering Department, Shoubra faculty of Engineering, Banha University, Abstact The effectiveness of manufacturing system is of the utmost importance. In the last three decades manufacturing systems effectiveness is attracting the researcher's interest as a key measure of considerable relevance for sustainable manufacturing. There are many research literatures on the Overall Equipment Effectiveness (OEE) and some on Overall Line Effectiveness (OLE), but these mostly deal with the technical aspects as a measure. There are very few case studies reported in OLE, these typically have the role of merely illustrating a particular aspect of the measurement or definition of OLE. In this work, it is identified that a lack of literature concerning the implementation of OLE, i.e. how to use it for the continuing operation systems and utilize it as a technical support for continuous improvement. This research reports on the results of a case study involving a multinational firm in Egypt that implementation of OLE measure on a continuous production line is a high sensitive measure and very efficient in isolating the deficiency causes Index Terms : Availability, Performance, Production system Quality, Overall line effectiveness, and Six big losses. I. INTRODUCTION Keeping a head, and markets competitions challenges are facing companies to implement various productivity improvement efforts to meet the needs of ever changing market demands. The total productive maintenance paradigm, launched by Nakajima in 1980's, has provided a quantitative metric for measuring productivity of an individual production component. The purpose of Overall Equipment Effectiveness (OEE) was firstly defined by Nakajima [1] in OEE utilized to evaluate the progress of the Total Productive Maintenance (TPM) philosophy through the measure of individual equipment. Nakajima recognized and categorized the main losses related to availability, performance, and quality. He established the six big losses as follows: a) Start up and yield losses at the early stage of production, b) Setup and adjustment for product mix change, c) Production losses when temporary malfunctions occur, d) Differences in equipment design speed and actual operating speed, e) Defects caused by malfunctioning equipment, and f) Poor productivity and lost yield due to poor quality. However, due to it is widely spreading in industrial use and effectiveness as a performance measurement for individual equipment. Further research has attempted to expand the application scope of OEE to entire processes, workshops, factories and production planets. In addition, its evaluating scope has also been expanded through the inclusion of more detailed elements of performance than just availability, performance and quality. Sherwin [2] proposed overall process effectiveness to measure the performance of whole production processes. Nachiappan and Anantharam [3] introduce a definition of Overall Line Effectiveness, in 2006, to evaluate the effectiveness of a continuous product line manufacturing system. They used a systematic methodology based on overall equipment effectiveness (OEE) metrics to model the productivity of a line manufacturing system in terms of OLE. Computer simulation was carried out for the evaluation of the OLE, identifying the bottle-neck machines and the effect of specific contributing parameter for improvement. Braglia et al. [4] presented overall equipment effectiveness of a manufacturing line to assess the performance of a production line. Oechsner et al. [5] proposed overall FAB effectiveness to measure the performance of an entire factory. On the other hand, Garza-Reyes [6] developed Overall Resource Effectiveness, which also considers material efficiency and variations in material and process cost as part of the evaluation of overall effectiveness, see also Garza- Reyes et al. [7]. Although OEE was mainly designed to monitor and control performance, Dal [8] suggests that the role of OEE goes far beyond the task of just monitoring and controlling. This is because OEE takes into account process improvement initiatives, prevents the sub-optimization of individual machines or production lines, provides a systematic method for establishing production targets, and incorporates practical management tools and techniques in order to achieve balanced view of process availability, performance and quality. Moreover, OEE can be used as an indicator of process improvement and as an approach to achieve it. Dal et al. [9] (2000), for example, used it to measure the improvement of a process within a manufacturing environment. Bamber et al. [10] remark that OEE is often used as a driver for improving the performance of a business by concentrating on quality, productivity and machine utilization issues and hence aimed

2 at reducing non-valued adding activities often inherent in manufacturing processes. In the case study by Dal et al. [9], it was reported that OEE not only helped to measure the improvement in the area in which it was implemented but also that it enabled new levels of performance measurement to be introduced Rehab [11] introduces a new application of OLE. The presented procedure used for monitoring and evaluation the production line using actual machine performance, the analysis based on the six major equipment-related losses. The proposed OLE application detects bottleneck in the production line, and give an accurate indicator to the proposed area of improvement. II. MATHEMATICAL MODEL DEFINITION Consider we are confronted with a complicated production line in a factory. The factory consists of n stations in m shops arranged in a conventional layout. This production line can manufacture up to q similar configuration products with different dimension. Raw material of the product come into the first station and pass different stations to reach to final inspection and packaging station. As a whole, production process can be divided into k different categories. The raw material comes into the first station spent some time and moves from station to another and from shop to another according to the sequence of manufacturing process. Different inspecting and checking stations and visual check audit quality of in process products. Product pass all stations consequently unless a defect is observed in checking stations which causes the product change its routine or continue from a previous station. Different raw material providers, staying time of raw material before entering the production line, variance of machines power, process requirements and etc. can cause defects in the product. Process time of stations is different. So in order to balance the production line, in some stations, parallel machines exist. Different size buffer between stations are considered to reduce station breakdowns effect on production rate and total availability of the line. In this study we aimed to develop a generalized calculation procedure for measuring the Overall Line Effectiveness OLE of the production line. For each station the overall equipment effectiveness OEE is presented and defined by Nakajima [1] as follows: (1) Where; A: Availability rate, P: Performance efficiency, and Q: Quality rate The availability rate factor measures the total time that the system is not operating because of breakdowns, set-up, adjustment, and other stoppages [12]. It is traditionally calculated using the Nakajima s formula presented below. In this formula, loading time refers to the equipment s total length of operation after any deduction of planned activities that may have disrupted production. (2) The performance rate, measures the ratio of the actual operating speed of the equipment to its ideal speed [12]. It can be calculated in a number of different ways. However, Nakajima measures a fixed amount of output, and in his definition of performance, it indicates the actual deviation in production in time from ideal cycle time. Performance (P) is calculated using Nakajima s equation: The quality (Q) indicates is the proportion of defective production to the total production volume. An important characteristic that should be noted is that the quality concept, as defined by Nakajima [1], only involves defects that occur in that designated stage of production, usually on a specific machine or production line and not elsewhere. Quality (Q) is calculated using the Nakajima s equation presented below: Each station breakdown doesn t lead to production line stop and hence any methodology which considers individual stations breakdown to calculate total availability time would not be precise. On the other hand, because of different product routes, it is not possible to derive line availability from production rate divided to line production capacity. To describe more, consider a corroded lot coming to the production line, so it is more likely that work piece get defected; so even if all stations are available, production rate is likely less than production line capacity. In order to overcome these conflicts and simulate the production line, a proposed mathematical model of the production line will be described herein after. A. Line Availability (A line ) Line availability A line is the ratio of full functional equipment s to all reparable (functioning and nonfunctioning) equipment within a known period along the production line. The three major obstacles to availability are breakdowns, downtime for maintenance, and changeovers. (5) n: number of equipment per line T Li : T Lossi : Loading time of machine number i total time loss of machine number i T bi : breakdown time of machine number i Tsi: setup time of machine number i (3) (4)

3 TLi: This total loading time is that the production line planned to be operation. PPT i : Planned production time for machine i PD i : Planned down time for machine i SL i : Scheduled time of operation for machine i B i : Scheduled time of breaks for machine i (6) Planned downtime time (PDi) is necessary for planned shutdowns needed for equipment maintenance, process clean up, periodic maintenance and normal product changeovers Breakdown time (Tbi) is the total production time lost due to unplanned shutdown (unplanned downtime) for the machines in production line. and Setup time (Tsi) is the total time loss due to setup and preparations B. Performance Efficiency (P line ): The performance rate is a measure of the ratio of the actual operating speed of the equipment and the ideal speed. Percentage performance is the ratio of what was actually produced in a given time over what would have been expected to be produced in a given time, and can be calculated as follows: (7) Toi: Operating time of machine number i T Lossesi : Losses time of machine number i T rdi : Idle time of machine number i T rsi : Reduced speed time of machine number i T rpi : Time loss for repair product produced from machine number C. Quality Rate (Q line ) In the present work, the quality rate (Q) considers the quality losses based on the number of items rejected due to quality defects. It is the number of good parts minus defective parts produced. (9) Pgi: good pieces of machine number i Ppi: Total pieces of machine number i Prji: Reject pieces of machine number i (10) Good pieces (Pgi) are the accepted product for total number of machine along the production line. Reject pieces (Pirj) is the startup rejects & production rejects units (scrap) for total number of machine. Total Pieces (Ppi) is the total product pieces for production line. Finally the Overall Line Effectiveness (OLE) of a generalized production line can be calculated as: (11) D. Case Study In this section we present the OLE implementation issues for a respective targeted company. The targeted company is one of the multinational leading manufacturers of sanitary ware in Egypt. Its manufacturing facility has quality management accreditation to ISO9001 and environmental management systems to ISA The company is a leading brand all over the world in bathtub industry and continues to show strong growth in global markets with retail, bulk products making their way into many countries around the world. Fig.1. shows a typical production line layout in the factory under consideration. The line was monitored for a long time enough to predict good time duration estimation for each process. (8) The operating time (Toi) is the time during which the production line is actually operating. Total idle time (Trdi) is the total losses time due to waiting time. Total repair time (Trpi) is the total Time loss for repair product produced from the whole production line. Total reduced speed time (Trsi) is the total losses time for the whole production line due to reduce speed. Fig.1. Production line layout Before OLE analysis, an initial discussion and possible implications from the collected data set will be presented herein after. It is depicted that the processes were limited in number and sequence flow is so simple, which make easy data collection and identify explicit linkage to other programs such as total production maintenance, continuous improvement and Lean to implying that OLE

4 needs to be considered as part of a program to change the organizations operational culture. In terms of drivers and motives to utilize OLE it is seen that intra/inter firm benchmarking and removal of waste (and identification of losses) were common responses. There was also with respect to the critical success factors for an implementation phase operator involvement, education and understanding, plus visibility of data/target were consistent responses. The implication of these responses is that although OLE provides a systematic approach to operational measures, the data must still be collected from the right place at the right time and then be displayed in the right format in the right locations before any discussion of CI can take place. The OLE response depends on the factors availability, performance and quality. In the case of availability, it is influenced by the (1) loading time and (2) unplanned downtimes elements; performance depends on the (3) ideal time and (4) operating cycle times; and quality depends on the (5) volume of defects generated in relation to the number of units produced. Therefore, these elements were varied in the study. The rest of the factors were excluded and assumed to be fixed because in practice they are easily defined and controlled. Value % 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 64% 78% 94% Fig.2. OLE and its factors values 47% Availability Performance Quality OLE According to Law and Kelton [13], unplanned downtime is the most important source of randomness for many manufacturing systems and it is associated with machine breakdowns or unscheduled downtime. Since the collected data to be analyzed, interpreted, and fitted. The settings that correspond to the variable unplanned downtime were identified by consulting the process experts. On the other hand, since the operating cycle time is carried out manually, it does vary considerably. Therefore, it was decided to be studied as a statistical input variable and its values selected by consulting the process experts and based on their experience related to the cycle time. Finally, the third element was modeled using a normal distribution because it is the most common shape expected by the data in terms of the quality of a product or process. Analysis and calculations conduced on the collected data show the overall line effectiveness is 46.84% which is very poor. Fig.2. illustrates the values of OLE factors as: the line availability is 64.17%, the line performance is 77.73% and the quality is 93.9%. Simply, it can be concluded that, availability improvement is the most responsive factor to improve the overall line effectiveness. In more details the availability analysis shows setup and adjustment time losses are equal to 178 hr/week, Breakdown time losses equal to 80 hr/week, and idle time loss are equal to 106 hr/week. Analysis of the setup time loss as per line shop shows the biggest setup time loss existed at the forming shop equal to 95hr as presented in Fig.3. Breakdown time loss analysis as per line shops identifies coating shop as the most breakdown time loser in the production line shops as shown in Fig.4. For the idle time losses it is found maximum at coating also as shown in Fig.5. Fig.3. Setup time loss as per line shops Fig.4. Breakdown time loss as per line shops Fig.5. idle time loss as per line shop

5 CONCLUSIONS The investigation presented in this study has established an OLE mathematical model very sensitive and reliable to apply for most if not all production lines. The OLE index measure expressed in the total line availability times the total line performance times the line production quality. The established investigation indicates that, if any improvement made in the capability measure will have a positive impact on OLE. For firms with multiple production lines, a motivation has also been to be able to compare lines based on OEE. However, as time passes the use of OLE is transformed to a system for analyzing production data to identify potential areas of improvement, and supporting lean initiatives. Thus, characteristically, OLE typically advances from a base measure for efficiency as the initial purpose, to being a tool to improve effectiveness for analyzing data to support Continuous Improvement objectives via the identification and elimination of waste. REFERENCES [1] Nakajima, S., An Introduction to TPM, Productivity Press, Portland, OR, [2] Sherwin, D., A review of overall models for maintenance management, Journal of Quality in Maintenance Engineering, Vol. 6 No. 3, pp , [3] Nachiappan, R.M. and Anantharam, N., Evaluation of overall line effectiveness (OLE) in a continuous product line manufacturing system, Journal of Manufacturing Technology Management, Vol. 17 No. 7, pp , [4] Braglia, M., Frosolini, M. and Zammori, F., Overall equipment effectiveness of a manufacturing line (OEEML): an integrated approach to assess systems performance, Journal of Manufacturing Technology Management, Vol. 20 No. 1, pp. 8-29, Journal of Operations & Production Management, Vol. 20 No. 12, pp , [10] Bamber, C., Castka, P. Sharp, J. and Motara, Y., Cross-functional team working for overall equipment effectiveness, Journal of Quality in Maintenance Engineering, Vol. 9 No. 3, pp , [11] Rehab E. Elbadwey, Automated Monitoring and Measuring Improvement of Production System Performance, thesis, Mechanical Engineering Department, Faculty of Engineering, Banha University, [12] Jonsson, P. and Lesshammar, M., Evaluation and improvement of manufacturing performance measurement systems the role of OEE, International Journal of Operations & Production Management, Vol. 19 No. 1, pp , [13] Law, A. and Kelton, W., Simulation Modeling and Analysis, 2nd ed., McGraw-Hill, New York, NY, BIOGRAPHY Rehab El-Sayed El-Badwey received a B.S. & M.Sc. Degree in Mechanical Engineering from Zagazig university and Banha University, Egypt in May 2003 & July She has been working as teaching assistant in mechanical engineering department in higher technological institute. And reliability official accounts in a system engineering group at Egyptian space program. And now she is studying for fulfillment of Ph.D. inoverall effectiveness analysis of low earth orbit remote sensing satellite from Al-Azhar university [5] Oechsner, R., Pfeffer, M., Pfitzner, L., Binder, H., Muller, E. and Vonderstrass, T., From overall equipment efficiency (OEE) to overall fab effectiveness (OFE), Material Science in Semiconductor Processing, Vol. 5 No. 4, pp , [6] Garza-Reyes, J.A., An investigation of OEE and development of the improved measures of performance OEE and ORE for manufacturing process management, PhD thesis, Manchester Business School, The University of Manchester, Manchester, [7] Garza-Reyes, J.A., Eldridge, S., Barber, K.D., Archer, E. and Peacock, T., Overall resource effectiveness (ORE) an improved approach for the measure of manufacturing effectiveness and support for decision-making, Proceedings of the 18th International Conference on Flexible Automation and Intelligent Manufacturing (FAIM), Sko vde, Sweden, 30 June-2 July, [8] Dal, B., Audit and review of manufacturing performance measures at Airbags International Limited, MSc dissertation, Manchester School of Management, UMIST, Manchester, [9] Dal, B., Tugwell, P. and Greatbanks, R., Overall equipment effectiveness as a measure of operational improvement a practical analysis, International