Article

Evaluation of overall line effectiveness (OLE) in a continuous product line manufacturing system

Authors:
To read the full-text of this research, you can request a copy directly from the authors.

Abstract

Purpose The total productive maintenance concept provided a quantitative metric – overall equipment effectiveness (OEE), for measuring the effectiveness of individual equipment in a factory, which is significant but insufficient since world class manufacturing (WCM) system focus on product line involving machines in series. This paper aims to present an approach to measure the overall line effectiveness (OLE) in continuous line‐manufacturing system. Design/methodology/approach Systematic methodology, based on OEE metrics, is developed to model the productivity of a line manufacturing system in terms of OLE. The general and step‐by‐step method of OLE measurement in the line consisting of n number of processes is explained using a flow chart. Findings Computer simulation has been carried out for the evaluation of OLE in product line manufacturing system with n number of machines. It also identifies the bottle‐neck machines and the effect of specific contributing parameter for improvement. Practical implications The result of this research makes it possible to represent the overall product line effectiveness as a benchmark for WCM to compare the performance of the various continuous product line manufacturing‐based industries. Originality/value This paper presents a successful and effective evaluation of OLE which will provide a useful guide to aspects of the production process where inefficiencies can be targeted for doing improvements in the product line.

No full-text available

Request Full-text Paper PDF

To read the full-text of this research,
you can request a copy directly from the authors.

... Overall Line Effectiveness (OLE) is a metric focused on measuring effectiveness of continuous line manufacturing systems [14]. ...
... As all machines are directly related together, the output of a machine is assessed by its contribution on the following one, which in turn links to the output of the upstream machines, leading to precise results in continues line manufacturing systems [14]. ...
... Requires more training and experience to implement than OEE. Also, if the planned downtime is not properly considered, it will lead to sub optimization of resources in the production line [14]. ...
Full-text available
Article
In recent decades, research has been done to better assess the effectiveness and efficiency of a production process. Therefore, some methods like Overall Equipment Effectiveness (OEE) or Overall Line Effectiveness (OLE) were proposed, however, none of the analysed methods has been able to illustrate accurate results in terms of adherence to a production plan. The purpose of this article is to present a metric, Global Process Effectiveness (GPE), based on OEE, that assesses effectiveness based on OEE factors and the schedule-adherence of a process to a pre-defined production plan, regarding product variety and quantity. If this new performance indicator replaces OEE, it will add one more goal to OEE without increasing the number of key performance indicators used in a company, giving a valuable assessment to operations planning experts.
... A considerable amount of research has been published in recent years [7] concerning the OEE, such as case studies, implementations, original OEE slightly modified, and new adaptations to other areas. The OEE has been adapted to various domains depending on industry need, such as line manufacturing [8], transport [9], mining equipment [10], and assembly tasks [11]. Authors proposed areas in which OEE could be applicable, in the service sector and logistics processes, such as goods reception or performing selection in a warehouse [1,12,13]. ...
... OEE is traditionally used to monitor production performances, but it can also be used as a metric for process improvement activities in other contexts [36]. In the production context, OEE has been used to measure the productivity and performance of a line manufacturing system by detecting and quantifying the line's critical points [8,37,38]. It has also been possible to measure losses of resources associated with human, material, and machine factors [39][40][41]. ...
... Some authors consider the original OEE insufficient [28], as it was only used to measure the effectiveness of particular equipment. Many manufacturers have customized OEE to fit their particular industrial requirements [8]. This study makes two strong contributions. ...
Full-text available
Article
The purpose of this paper is to build up and implement a framework of a lean performance indicator with collaborative participation. A new indicator derived from OEE is presented, overall process effectiveness (OPE), which measures the effectiveness of an operation process. The action research (AR) methodology was used; collaborative work was done between researchers and management team participation. The framework was developed with the researchers’ and practitioners’ experiences, and the data was collected and analyzed; some improvements were applied and finally, a critical reflection of the process was done. This new metric contributes to measuring the unloading process, identifying losses, and generating continuous improvement plans tailored to organizational needs, increasing their market competitiveness and reducing the non-value-add activities. The OEE framework is implemented in a new domain, opening a new line of research applied to logistic process performance. This framework contributes to recording and measuring the data of one unloading area and could be extrapolated to other domains for lean performance. It was possible to generate and validate knowledge applied in the field. This study makes collaborative participation providing an effectiveness indicator that helps the managerial team to make better decisions through AR methodology.
... Overall Line Effectiveness (OLE) is a metric focused on measuring effectiveness of continuous line manufacturing systems [14]. ...
... As all machines are directly related together, the output of a machine is assessed by its contribution on the following one, which in turn links to the output of the upstream machines, leading to precise results in continues line manufacturing systems [14]. ...
... Requires more training and experience to implement than OEE. Also, if the planned downtime is not properly considered, it will lead to sub optimization of resources in the production line [14]. ...
Full-text available
Article
In recent decades, research has been done to better assess the effectiveness and efficiency of a production process. Therefore, some methods like Overall Equipment Effectiveness (OEE) or Overall Line Effectiveness (OLE) were proposed, however, none of the analysed methods has been able to illustrate accurate results in terms of adherence to a production plan. The purpose of this article is to present a metric, Global Process Effectiveness (GPE), based on OEE, that assesses effectiveness based on OEE factors and the schedule-adherence of a process to a pre-defined production plan, regarding product variety and quantity. If this new performance indicator replaces OEE, it will add one more goal to OEE without increasing the number of key performance indicators used in a company, giving a valuable assessment to operations planning experts.
... 2. Theoretical background OEE was proposed by Nakajima (1988) as part of the Total Productive Maintenance (TPM) approach. It represents a quantitative metric to identify and measure the productivity of individual equipment (En-Nhaili et al., 2016;Muchiri and Pintelon, 2008;Nachiappan and Anantharaman, 2006;Ng Corrales et al., 2020;Zhou et al., 2020). ...
... OEE measures only the effectiveness of planned production schedules, not considering planned shutdown losses (e.g. downtime for scheduled maintenance activities), that could be particularly relevant in capitalintensive and continuous line manufacturing industries (Jeong and Phillips, 2001;Mathur et al., 2011;Nachiappan and Anantharaman, 2006). To overcome this limitation and account also for losses that impede equipment loading time, another KPI was proposed, called Overall Plant Efficiency (OPE), which measures the OEE relative to every minute of the clock, including planned downtime (Hansen, 2002). ...
Purpose This paper aims to overcome the inability of both comparing loss costs and accounting for production resource losses of Overall Equipment Effectiveness (OEE)-related approaches. Design/methodology/approach The authors conducted a literature review about the studies focusing on approaches combining OEE with monetary units and/or resource issues. The authors developed an approach based on Overall Equipment Cost Loss (OECL), introducing a component for the production resource consumption of a machine. A real case study about a smart multicenter three-spindle machine is used to test the applicability of the approach. Findings The paper proposes Resource Overall Equipment Cost Loss (ROECL), i.e. a new KPI expressed in monetary units that represents the total cost of losses (including production resource ones) caused by inefficiencies and deviations of the machine or equipment from its optimal operating status occurring over a specific time period. ROECL enables to quantify the variation of the product cost occurring when a machine or equipment changes its health status and to determine the actual product cost for a given production order. In the analysed case study, the most critical production orders showed an actual production cost about 60% higher than the minimal cost possible under the most efficient operating conditions. Originality/value The proposed approach may support both production and cost accounting managers during the identification of areas requiring attention and representing opportunities for improvement in terms of availability, performance, quality, and resource losses.
... To be as efficient as possible, these impacts must be considered to enable maintenance to use its limited resources where they are most needed in a production system [3]. To achieve this, the focus has to change from key figures looking at the availability of individual machines to key figures with a holistic approach [4]. The aim of this publication is to provide transparency and present the difference between key figures focusing on the availability of machines and on the availability of the production system. ...
... In addition to the different ways availability is calculated for a single machine, there are also different opinions on how availability should be calculated for a production system. Nachiappan and Anantharaman have summarized the different viewpoints in their publication: either the average of the individual machine availabilities is used without looking at their interactions, or the environment of the production system is included [4]. Multiple approaches to predicting or measuring the availability, including the interactions of machines, are presented in the thesis by Sun [13], well summarized by Bourouni in his availability analysis of an osmosis plant [14]. ...
... Overall throughput effectiveness (OTE) was proposed via defining types of production lines including series, parallel, assembly and expansion that allows bottleneck detection and identification of hidden capacity [18][19][20]. In addition, viewing line efficiency, Nachiappan and Anantharaman [21] Electronic copy available at: https://ssrn.com/abstract=4086696 proposed overall line effectiveness (OLE) with the line availability (LA) and line production quality efficiency (LPQP). ...
... Thus, due to inventory policies, not all proceeded products are delivered. To evaluate the actual gap between capacity planning and demand planning, Equation (7) The status stack charts of CLIP are obtained from the structure of OSCE as illustrated in Fig. 3. Through examining the status stack charts, OEE and OGE [21] can be employed. CLIP considers unfulfilled demand, including present backlog that denotes insufficient capacity for demand planning and DEW that identifies instabilities or nervousness for CD or ATP. ...
Full-text available
Article
Symbolic supply chain incorporates collaborative supply chain management, which aims to maintain economic sustainability by reducing waste and achieving profitability from resource utility. Semiconductor supply chain is increasingly complicated due to shortening product life cycles, demand fluctuation and series of collaborative decisions, yet little research focuses on risk assessment from internal operational planning to end-to-end supply chain to model network resilience for symbiotic supply chains. Limitations of existing approaches can be traced in part to the lack of a framework within which the decisions involved in different levels can be integrated and aligned in light of supply chains dynamics. This study aims to develop a risk assessment framework from intercorporate planning level to overall supply chain effectiveness. The proposed framework integrates hierarchical decisions and ensures the interoperability via an ontology for planning and control decisions. Focusing on realistic needs of supply chain management, a model is constructed based on backend production and the related supply chain information from a leading semiconductor company in Germany for validation. The results have shown practical viability of how the collaboration among series of planning entities impact on symbiotic supply chain significantly.
... The lean bundle OEE approach proposed by Shah and Ward (Shah and Ward, 2003), considered plant size only while plant integration and aging were neglected. Waste generation and workspace (ergonomic) condition were neglected in many other studies related to traditional OEE measures (Wilson, 2010;Ljungberg, 1998;Madhavan et al., 2011;Nachiappan and Anantharaman, 2006;Muchiri and Pintelon, 2008;Wang and Pan, 2011;Andersson and Bellgran, 2015;Binti Aminuddin et al., 2016). ...
... The traditional OEE has given birth to new OEE models which are either static/deterministic or stochastic in nature. The new static/deterministic OEE measures include: Overall Asset Effectiveness (OAE), Overall Plant Effectiveness (OPE), Total Equipment Effectiveness Performance (TEEP), Production Equipment Effectiveness (PEE) and Overall Factory Effectiveness (OFE) applied in packaging and chemical processing (Muchiri and Pintelon, 2008); Doubly Weighted Grouping Efficiency (DWGE) applied in cellular manufacturing systems (Sarker, 2001); Overall Line Effectiveness (OLE) for automobile industries (Nachiappan and Anantharaman, 2006); Overall Equipment Effectiveness of a Manufacturing Line (OEEML) for automobile firm (Braglia et al., 2009); Global Production Effectiveness (GPE) for global manufacturing system (Lanza et al., 2013); Overall Throughput Effectiveness (OTE) for wafer fab and glass firm (Muthiah et al., 2008); Overall Equipment Effectiveness Market-Based (OEEMB) for iron and steel industry (Anvari et al., 2010); Equipment Performance and Reliability (EPR) model for semiconductor production system (Samat et al., 2012); Rank-Order Centroid (ROC) method in Overall Weighting Equipment Effectiveness (OWEE) for fiber cement roof production system (Wudhikarn, 2010); Overall Equipment and Quality Cost Loss (OEQCL) for fiber cement manufacturing system (Wudhikarn, 2012); OEE and Productivity measure (OEEP) in automobile industry (Andersson and Bellgran, 2015); and OEE-Total Productive Maintenance (TPM) and Lean Manufacturing (LM) measures in manufacturing systems (Binti Aminuddin et al., 2016). The stochastic OEE models evolved include: Probability density function (Normal and Beta distributions) of OEE applied to waterproofing coatings firm (Zammori et al., 2010); and the simulation-based Taguchi method in weighted OEE for crimping manufacturing line (Yuniawan et al., 2013). ...
Full-text available
Article
The global demand for effective utilization of both humans and machinery is increasing due to wastage incurred during product manufacturing. Excessive waste generation has made entrepreneurs find it difficult to breakeven. The development of dynamic error-proof Overall Equipment Effectiveness (OEE) model for optimizing a complex production process is targeted at minimizing/eradicating operational wastes/losses. In this study, the error-proof sigma metric was integrated into the extended traditional OEE factors (availability, performance, quality) to include losses due to waste and man-machine relationships. Error-proof sigma statistics enabled continuous corrective measures on unsatisfactory or low-level OEE resulted from process output variations (quantity delivered or expected), which were mapped into sigma statistical standards (one-to six-sigma). Application of the model in a processing company showed that errors of the process were reduced by 78% and 42% respectively for traditional OEE and the new Error-Proof OEE (OEE-EP). The results revealed that the OEE-EP model is better than the other existing schemes in terms of losses elimination in the production process. ‫اﻹﻧﺘﺎج‬ ‫ﻋﻤﻠﯿﺔ‬ ‫ﻟﺘﺤﺴﯿﻦ‬ ‫ﻟﻠﻤﻌﺪات‬ ‫اﻟﻌﺎﻣﺔ‬ ‫ﺑﺎﻟﻔﻌﺎﻟﯿﺔ‬ ‫اﻟﻤﺘﻌﻠﻘﺔ‬ ‫اﻟﺪﯾﻨﺎﻣﯿﻜﯿﺔ‬ ‫اﻷﺧﻄﺎء‬ ‫ﺗﺠﻨﺐ‬ ‫ﻧﻤﻮذج‬ ‫ﺗﻄﻮﯾﺮ‬ ‫ﻛﺮﯾﻢ‬ ‫ب.‬ ، ‫أ‬. ‫أ‬ ‫ﻻﺑﻲ‬ ، ‫أوﺟﯿﻨﺠﺒﻲ‬ ‫ت.‬ ، ‫ب.‬ ‫أ‬ ‫ﻛﯿﻨﻮﻟﻲ‬ ، ‫و.‬ ‫أ‬ ‫إدرﯾﺲ‬ ‫م.‬ ‫و‬ ‫دﯾﺮوﺑﺎ‬ ‫اﻟﻤﻠﺨﺺ‬ : ‫ﺣﯿﺚ‬ ‫اﻟﺘﺼﻨﯿﻊ‬ ‫ﻋﻤﻠﯿﺔ‬ ‫أﺛﻨﺎء‬ ‫ﯾﺤﺪث‬ ‫اﻟﺬي‬ ‫ﻟﻠﮭﺪر‬ ً ‫ﺗﺠﻨﺒﺎ‬ ‫واﻵﻻت‬ ‫اﻟﻌﺎﻣﻠﺔ‬ ‫ﻟﻠﻘﻮى‬ ‫اﻟﻔﻌﺎل‬ ‫اﻻﺳﺘﺨﺪام‬ ‫ﻋﻠﻰ‬ ‫اﻟﻌﺎﻟﻤﻲ‬ ‫اﻟﻄﻠﺐ‬ ‫ﯾﺘﺰاﯾﺪ‬ ‫ﻣﺮﺿﯿﺔ‬ ‫ﺗﺼﻨﯿﻌﯿﺔ‬ ‫ﻧﺘﺎﺋﺞ‬ ‫ﺗﺤﻘﯿﻖ‬ ‫ﻋﻤﻠﯿﺔ‬ ‫ﻣﻦ‬ ‫ﻟﻠﻨﻔﺎﯾﺎت‬ ‫اﻟﻤﻔﺮط‬ ‫اﻟﺘﻮﻟﯿﺪ‬ ‫ﯾﺼﻌﺐ‬. ‫ﻧﻤﻮذج‬ ‫ﺗﻄﻮﯾﺮ‬ ‫ﯾﺴﺘﮭﺪف‬ ‫اﻟﺪﯾﻨﺎﻣﯿﻜﯿﺔ‬ ‫اﻷﺧﻄﺎء‬ ‫ﺗﺠﻨﺐ‬ ‫اﻟﺘﺸﻐﯿﻠﯿﺔ‬ ‫اﻟﺨﺴﺎﺋﺮ‬ ‫و‬ ‫اﻟﻨﻔﺎﯾﺎت‬ ‫ﺗﻘﻠﯿﻞ‬ ‫طﺮﯾﻖ‬ ‫ﻋﻦ‬ ‫اﻟﻤﻌﻘﺪة‬ ‫اﻹﻧﺘﺎج‬ ‫ﻋﻤﻠﯿﺔ‬ ‫ﻣﻦ‬ ‫اﻟﻤﺜﻠﻰ‬ ‫اﻻﺳﺘﻔﺎدة‬ ‫ﺗﺤﻘﯿﻖ‬ ‫ﻟﻠﻤﻌﺪات‬ ‫اﻟﻌﺎﻣﺔ‬ ‫ﺑﺎﻟﻔﻌﺎﻟﯿﺔ‬ ‫اﻟﻤﺘﻌﻠﻘﺔ‬ ‫ﻣﻤﻜﻦ‬ ‫ﺣﺪ‬ ‫أدﻧﻰ‬ ‫إﻟﻰ‬. ‫اﻟﻔﻌﺎﻟﯿﺔ‬ ‫ﻧﻈﺎم‬ ‫ﻓﻲ‬ ‫اﻟﻤﺆﺛﺮة‬ ‫اﻟﻌﻮاﻣﻞ‬ ‫ﻻﺧﺘﺒﺎر‬ ‫ﻟﻠﺨﻄﺄ‬ ‫اﻟﻤﻘﺎوم‬ ‫ﺳﯿﺠﻤﺎ‬ ‫ﻗﯿﺎس‬ ‫اﻟﺪراﺳﺔ‬ ‫ھﺬه‬ ‫ﻓﻲ‬ ‫اﺳﺘﺨﺪم‬ ‫ﻟﻠﻤﻌﺪات‬ ‫اﻟﻌﺎﻣﺔ‬ ‫اﻟﻤﺘﻌﻠﻘﺔ‬ ‫ﺳﯿﺠﻤﺎ‬ ‫إﺣﺼﺎﺋﯿﺎت‬ ‫أﺗﺎﺣﺖ‬ ‫واﻵﻟﺔ.‬ ‫اﻹﻧﺴﺎن‬ ‫ﺑﯿﻦ‬ ‫واﻟﻌﻼﻗﺔ‬ ‫اﻟﻨﻔﺎﯾﺎت‬ ‫ﻋﻦ‬ ‫اﻟﻨﺎﺟﻤﺔ‬ ‫اﻟﺨﺴﺎﺋﺮ‬ ‫ﻟﺘﻀﻤﯿﻦ‬ ‫واﻟﺠﻮدة(‬ ‫واﻷداء‬ ‫)اﻟﺘﻮﻓﺮ‬ ‫ﻧﺘﯿﺠﺔ‬ ‫ﻟﻠﻤﻌﺪات‬ ‫اﻟﻌﺎﻣﺔ‬ ‫اﻟﻔﻌﺎﻟﯿﺔ‬ ‫ﻧﻈﺎم‬ ‫ﻣﻦ‬ ‫ﻣﻨﺨﻔﺾ‬ ‫أو‬ ٍ ‫ﺮض‬ ُ ‫ﻣ‬ ‫ﻏﯿﺮ‬ ‫ﻣﺴﺘﻮى‬ ‫ﻋﻠﻰ‬ ‫ﻣﺴﺘﻤﺮة‬ ‫ﺗﺼﺤﯿﺤﯿﺔ‬ ‫إﺟﺮاءات‬ ‫اﺗﺨﺎذ‬ ‫اﻷﺧﻄﺎء‬ ‫ﺑﺘﺠﻨﺐ‬ ‫ﻓ‬ ‫ﻟﻼﺧﺘﻼﻓﺎت‬) ‫ﻟﻠﺴﯿﺠﻤﺎ‬ ‫إﺣﺼﺎﺋﯿﺔ‬ ‫ﻣﻌﺎﯾﯿﺮ‬ ‫إﻟﻰ‬ ‫ﺗﻌﯿﯿﻨﮭﺎ‬ ‫ﺗﻢ‬ ‫واﻟﺘﻲ‬ ‫اﻟﻤﺘﻮﻗﻌﺔ(،‬ ‫أو‬ ‫ﺗﺴﻠﯿﻤﮭﺎ‬ ‫ﺗﻢ‬ ‫اﻟﺘﻲ‬ ‫)اﻟﻜﻤﯿﺔ‬ ‫اﻟﻌﻤﻠﯿﺔ‬ ‫ﻧﺎﺗﺞ‬ ‫ﻲ‬ 1-6 ‫ﺳﯿﺠﻤﺎ(.‬ ‫اﻟﺸﺮﻛﺎت‬ ‫إﺣﺪى‬ ‫ﻓﻲ‬ ‫اﻟﻨﻤﻮذج‬ ‫ﺗﻄﺒﯿﻖ‬ ‫أظﮭﺮ‬ ‫وﻗﺪ‬ ‫اﻟﻤﻌﺎﻟﺠﺔ‬ ‫ﺑﻨﺴﺒﺔ‬ ‫اﻧﺨﻔﻀﺖ‬ ‫اﻟﻌﻤﻠﯿﺔ‬ ‫أﺧﻄﺎء‬ ‫أن‬ 78 ‫و‬ % 42 ‫ﺑﺎﻟﻨﺴﺒﺔ‬ ‫اﻟﺘﻮاﻟﻲ‬ ‫ﻋﻠﻰ‬ % ‫اﻟﺘﻘﻠﯿﺪي‬ ‫ﻟﻠﻤﻌﺪات‬ ‫اﻟﻌﺎﻣﺔ‬ ‫اﻟﻔﻌﺎﻟﯿﺔ‬ ‫ﻟﻨﻈﺎم‬ ‫ﻟﻠﻤﻌﺪات‬ ‫اﻟﻌﺎﻣﺔ‬ ‫ﺑﺎﻟﻔﻌﺎﻟﯿﺔ‬ ‫اﻟﻤﺘﻌﻠﻘﺔ‬ ‫اﻟﺪﯾﻨﺎﻣﯿﻜﯿﺔ‬ ‫اﻷﺧﻄﺎء‬ ‫ﺗﺠﻨﺐ‬ ‫ﻟﻨﻈﺎم‬ ‫اﻟﺠﺪﯾﺪ‬ ‫اﻟﻨﻤﻮذج‬ ‫و‬. ‫ﻟﻠﻤﻌﺪات‬ ‫اﻟﻌﺎﻣﺔ‬ ‫ﺑﺎﻟﻔﻌﺎﻟﯿﺔ‬ ‫اﻟﻤﺘﻌﻠﻘﺔ‬ ‫اﻟﺪﯾﻨﺎﻣﯿﻜﯿﺔ‬ ‫اﻷﺧﻄﺎء‬ ‫ﺗﺠﻨﺐ‬ ‫ﻧﻤﻮذج‬ ‫أن‬ ‫اﻟﻨﺘﺎﺋﺞ‬ ‫وﻛﺸﻔﺖ‬ ‫ﺣﯿﺚ‬ ‫ﻣﻦ‬ ‫اﻷﺧﺮى‬ ‫اﻟﻤﺨﻄﻄﺎت‬ ‫ﻣﻦ‬ ‫أﻓﻀﻞ‬ ‫اﻹﻧﺘﺎج‬ ‫ﻋﻤﻠﯿﺔ‬ ‫ﻓﻲ‬ ‫اﻟﺨﺴﺎﺋﺮ‬ ‫ﻣﻦ‬ ‫اﻟﺘﺨﻠﺺ‬. ‫اﻟﻜﻠﻤﺎت‬ ‫اﻟﻤﻔﺘﺎﺣﯿﺔ‬ : ‫دﯾﻨ‬ ‫ﺎﻣﯿﻜﯿﺔ‬ ‫ﺳﯿﺠﻤﺎ؛‬ ‫ﻣﻘﯿﺎس‬ ‫ﻟﻠﻤﻌﺪات؛‬ ‫اﻟﻌﺎﻣﺔ‬ ‫اﻟﻔﻌﺎﻟﯿﺔ‬ ‫اﻟﻌﻤﻠﯿﺔ‬ ‫ﺗﻜﺎﻣﻞ‬ ‫؛‬ ‫اﻹﻧﺘﺎﺟﯿﺔ‬. Development of OEE Error-proof (OEE-EP) Model for Production Process Improvement 60 NOMENCLATURE G Quality products delivered per unit time (year) p G Total quantity produced per unit time (year) w G Actual waste generated (%) i Counter for overall equipment effectiveness factor j Counter for sigma value n Traditional scheme (n =3), new scheme (n =5) OEE Overall Equipment Effectiveness c OEE Effectiveness improvement factor (%) w P Planned (expected) waste (%) 1 t Actual production volume per unit time (year) 2 t Planned production volume per unit time (year) 0 t Actual system performance per unit time (year) n t System performance expected per unit time (year) a T Actual human productivity per unit time (year) s T Expected human productivity per unit time (year) x Time (year) i y Equipment effectiveness factor at a given year, x ' y i Contribution of equip. effectiveness factors (%) β Performance efficiency of equipment (%) σ Improvement (error-proof) factor α Availability efficiency of production equip. (%) μ Quality rate (efficiency) of products (output) (%) ω Waste generation rate (efficiency) of equip. (%) γ Human/ergonomics-equipment efficiency (%) EP Error-proof MSE Mean Square Error
... The performance evaluation of the production process motivates managers to make better decisions about how to manage and improve the production system performance effectively [3]. Companies can achieve this by determining metrics that are suitable for measurement objectives [4]. Overall Equipment Effectiveness (OEE) is a performance measurement method that is used to monitor and control the performance of production equipment [5]. ...
... A good number of items is a reduction in the total number of items produced by the number of defective and rejected items. The equation for calculating quality is presented in (4): Quality = Total items -defects amount Total items (4) In this research, DES is proposed as an approach to analyze the OEE because it is suitable to enact the system that has a queue network as well as to compare and predict the scenario and focus on the process that involves the use of a queue. General procedures of developing DES are shown in Figure 1. the procedures, the explanation of the proposed DES production model of Finishing Division is summarized as follow. ...
Full-text available
Article
To measure the performance of the production process, an efficiency calculation is performed using the Overall Equipment Effectiveness (OEE) method. OEE can measure various production losses and identify potential developments that can be carried out in a production process. This research is expected to be an input to improve production efficiency. The results of overall equipment effectiveness are then performed using Discrete Event Simulation, which built using STELLA Architect. The result shows that their overall equipment effectiveness scores are below the company goals, and performance rate is their lowest score. These simulation results are expected to be a basis for improvements in the production division, especially at Table Tennis Table Manufacturer.
... Literature indicates that overall line effectiveness is used to identify bottlenecks in equipment performance and to measure the production rate of a production line system, which consists of multiple manufacturing processes, on the basis of OEE [6]. However, this measurement fails to simultaneously identify material loss and waste. ...
... Overburr, scratch/pinch/crush of surface, insufficient length, overswing, turning vibration mark 5 insufficient width/length/remaining length of the wing, wings not placed in the middle, Unfavorable R angles and cracks. 6 Hardness, torsion, bending, edge bruising 7 ...
Article
Business operations must effectively utilize real-time information presented by the manufacturing execution system for the identification and analysis of key improvements, finite resources can be put to better use, and implement effective quality improvements. This study analyzed overall loss through time loss, performance loss, and material loss by expanding the manufacture value process. Overall process loss (OPL) and overall line loss (OLL) were developed as indexes to select key improvement targets. First, Pareto analysis was used to identify process. The ICAM (Integrated Computer Aided Manufacturing) Definition (IDEF) was then employed to inform improvement decisions regarding the loss item. Finally, new key improvement goals were identified from the updated OLL, with continual improvement. The contributions of this study are the development of the indexes, OPL and OLL, based on overall loss, the improvement decision model, and the construction of a mechanism whereby sustainable reduction of overall loss can be achieved.
... Many industries have customized it to fit to their particular requirements [11]. Based on the OEE structure, models have been developed for domains such as sustainability [12], line manufacturing [13,14], assets [10], resources [15], transport [16,17] and ports [18]. ...
... Initially, OEE was used in production, in particular for TPM, which assists in identifying the overall equipment performance in a manufacturing process [199]. To accommodate industry needs, some researchers began to analyze the productivity of manufacturing line systems [6,13] or factories [174]. Currently, OEE is used with continuous improvement methodologies, such as lean manufacturing to increase productivity by eliminating waste [200]. ...
Full-text available
Article
Overall equipment effectiveness (OEE) is a key performance indicator used to measure equipment productivity. The purpose of this study is to review and analyze the evolution of OEE, present modifications made over the original model and identify future development areas. This paper presents a systematic literature review; a structured and transparent study is performed by establishing procedures and criteria that must be followed for selecting relevant evidences and addressing research questions effectively. In a general search, 862 articles were obtained; after eliminating duplicates and applying certain inclusion and exclusion criteria, 186 articles were used for this review. This research presents three principal results: (1) The academic interest in this topic has increased over the last five years and the keywords have evolved from being related to maintenance and production, to being related to lean manufacturing and optimization; (2) A list of authors who have developed models based on OEE has been created; and (3) OEE is an emerging topic in areas such as logistics and services. To the best of our knowledge, no comparable review has been published recently. This research serves as a basis for future relevant studies.
... Numerous metrics are utilized by companies to evaluate their performance to keep their position stable in globally competitive markets [1]. Industries are unable to identify opportunities to improve their performance unless they collect and analyse the relevant data of their current performance [2]. ...
... Relationship between equipment timing and the six big losses[2] Fig. 1 illustrates the relationship between equipment timing and the six big losses. OEE can be measured based on the following equation[1]: ...
Full-text available
Article
Companies are required to integrate a set of critical dimensions to measure and evaluate their performance to compete in globally competitive markets. Overall Equipment Effectiveness (OEE) and Overall Resource Effectiveness (ORE) are two quantitative metrics attempt to measure and improve the effectiveness of manufacturing operations. This study aims to use the concept of OEE and ORE to evaluate and monitor the performance of a concrete block manufacturing system at Dler Company in Iraq. The study was conducted in two years of operation during 2016 and 2017. Results from 2016 show that the level of effectiveness was lower than the world-class. An improvement in the average value of the OEE for 2017 was recorded, where the OEE value is increased in 2017 to 75% in comparison with its value in 2016 which was 67%. While the ORE value is increased in 2017 to 66% in comparison with its value in 2016 at 59%. It was also found that the reasons for this improvement are due to the enhancement that is made in the availability and quality.
... In their article [29], the authors discuss a production system based on a continuous product line. In this situation, it is necessary to focus not only on the performance of a single machine, but also on the performance of the line. ...
Full-text available
Article
Competitiveness has reached outstanding levels in every marketplace sector. The Overall Equipment Effectiveness ( OEE ) framework has gained increasing interest in different contexts as a leading measure for improving production. The application of the OEE as a driver of improvement is an extensively covered topic in scientific literature. However, the existing research mainly focuses on case studies showing the obtained results on specific applications, typically in the manufacturing domain. This paper proposes the adaptation of the OEE framework for its application to service companies. The obtainable results and the involved possible errors in the use of the framework are addressed. We show the benefits of the proposed framework for allowing a meaningful comparison of employees performing different types of activities. Inefficiencies can be identified and classified by associating them with causes. Methods, procedures and territorial aspects are related in general to the company organization. The application of the OEE transforms the measures from targets to drivers of improvements by identifying the areas of loss in the process. The proposed framework is demonstrated on a case study consisting in a service company operating in the telecommunication’s field. The evaluation has been done over a time-span of 12 months, addressing the behavior of 952 employees. The analysis allows producing results that are useful to assess the behavior of the company. In particular, we can distinguish the causes of losses either related to the employees and the ones related to the company. Different types of losses are quantified, and this information can be used to optimize the various aspects in details. The assessment of the losses enables the comparison of the performance among different areas and different employees.
... Similarly, the differences in work posture ratings could be connected with the high production volume and pace in Lines 1 and 3. In a similar European company, it was found that the higher the manufacturing pace and volume, the more the workers adapted poor body postures (Nachiappan and Anantharaman, 2006). Moreover, differences in the perceptions about the quality and adequacy of training could be attributed to the fact that during the period this study was conducted, the company hired several workers to increase its production volumes, which created additional pressure to provide training. ...
Full-text available
Conference Paper
This study was performed at a truck manufacturing company with three production lines. It employed a paper-based survey with two demographic questions, 16 Likert-type questions covering physical, cognitive and organisational human factors, and three qualitative questions to invite workers to state improvement ideas and current challenges and strengths. The response rate was 35%. The median across the 70 completed surveys for all human factors areas investigated was M=3 out of 4 maximum, except for the quality of instructions (M=2), physical load demands (M=2) and job variety (M=4). Statistically significant differences amongst the three production lines were observed for four human factors aspects. The years of work experience in the company were found significantly and negatively correlated with three human factors aspects. Most of the improvements suggested by the workers were related to organisational and procedural aspects. A similar focus was revealed for the challenges met, whereas collegial relationships were appreciated as the strongest area.
... Past scholars have developed some metrics for measuring the performance of various sections of the production cycle. For instance, OLE (overall line effectiveness) was proposed by Nachiappan and Anantharaman (2006) to assess the continuous line manufacturing system performance with the assumption that OEE can only be used for individual machines rather than the overall machine assembly. Similarly, Garza-Reyes (2015) developed ORE (overall resource effectiveness) after realising that OEE does not account for the efficient use of resources and materials. ...
Article
Purpose Equipment performance helps the manufacturing sector achieve operational and financial improvements despite process variations. However, the literature lacks a clear index or metric to quantify the monetary advantages of enhanced equipment performance. Thus, the paper presents two innovative monetary performance measures to estimate the financial advantages of enhancing equipment performance by isolating the effect of manufacturing fluctuations such as product mix price, direct and indirect characteristics, and cost changes. The research provides two measures, ISB (Improvement Saving Benefits) and IEB (Improvement Earning Benefits), to assess equipment performance improvements. The effectiveness of the metrics is validated through a three stages approach, namely: (1) experts' binary opinion, (2) sample, and (3) actual cases. The relevant data may be collected through accounting systems, purpose-built software, or electronic spreadsheets. The findings suggest that both measures provide an effective cost-benefit analysis of equipment performance enhancement. The measure ISB indicates savings from performance increases when equipment capacity is greater than product demand. IEB is utilised when equipment capacity is less than product demand. Both measurements may replace the unitary cost variation, which is subject to manufacturing changes. Manufacturing businesses may utilise the ISB and IEB metrics to conduct a systematic analysis of equipment performance and to appreciate the financial savings perspective in order to emphasis profitability in the short and long term. The study introduces two novel financial equipment performance improvement indicators that distinguish the effects of manufacturing variations. Manufacturing variations cause cost advantages from operational improvements to be misrepresented. There is currently no approach for manufacturing organisations to calculate the financial advantages of enhancing equipment performance while isolating production irregularities.
... In the product-based production layout, three monitoring systems have been identified which are overall line effectiveness (OLE), FPY and DPMO. According to Nachiappan and Anantharaman (2006), through their study of comparing the performance of the various continuous product line manufacturing-based industries, OLE can be considered as a continuous product line manufacturing system. The FPY is concerned about the productivity and on-time customer delivery according to the flow of Enhancement of the efficiency of internal supply chain production system 337 the connected production system (Antony et al., 2012), while DPMO is a measure of the defect for the overall production system, beginning from the first assembly process until the end of the process (Ravichandran, 2007) and it is more towards measuring the quality of the product-based production system as it involves with entire connected processes. ...
... Shutdowns are defined as the situation when yield is zero and the system generates nothing, or when the machine works and yet produces nothing during the annual inspection, and they are caused by two factors: a breakdown loss, which keeps referring to parts letdown that requires repair or replacement and the losses are caused by two factors: a breakdown loss, which alludes to parts failure that necessitates restoration, and the losses are caused by two factors: a breakdown loss, which demands fixing, and the deficits are triggered (Anantharaman and Nachiappan, 2006). For illustration, the beginning of fabrication or the start of split jobs, product revisions and also the status of the operation. ...
Full-text available
Article
Purpose The purpose of this research is to discover equipment losses and assess the accomplishment of overall equipment effectiveness (OEE) values. Design/methodology/approach Industries specialized in die shops often have issues regarding their efficiencies, conferring to statistics further production line department procedure for various machines frequently suffered restrictions owing to excessive downtime and speed losses in machines thus, reducing their effectiveness and efficiency. OEE is a means of determining how effective a piece of equipment is when in working condition. Calculation of OEE finds the heart of the issue and the root cause for the underlying problem. Findings The dimensional outcomes suggest that the average machine effectiveness has not attained the norm of >85%, but there is still room for progression. Originality/value One recommended procedure to reduce losses is to keep the actual pace of operation and downtime of equipment constant. Many such suggestions are provided to reduce the losses.
... 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. ...
Full-text available
Article
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
... 3 of the indicator not only consider the equipment performance, otherwise identifies the bottleneck and measure the factory level performance [12,13]. Following this direction, other authors adapted the indicator to measure the performance of the production line in the manufacturing system [14,15]. The mining sector also has implemented OEE to measure the performance of their equipment (e.g. ...
Full-text available
Article
Productivity is a measurement of the efficiency of the resources, constant changes in the environment promote that companies regularly measure their activities. This study aims to define a new metric based conceptually on the traditional OEE. The new indicator measures the efficiency of logistic equipment in this case a system of automated handling: automated guided vehicles (AGVs). The indicator was developed by parameterizing the variables and defining the losses found in the process. It was applied in a factory of car parts and spare parts, two AVGs were analyzed in two different routes. The new indicator was effectively adapted to measure the performance of a logistics equipment. The OEE agv results obtained are those expected by the company, although some authors value the indicator with a higher percentage. Also, the result has shown that a new route could be added for more efficient use of the two AGVs.
... As is known to all that overall equipment efficiency (OEE, proposed by Nakajima [20]) is widely used in factories to evaluate individual equipment performance in a production system. On this basis, due to the importance of production line overall effectiveness improvement, OLE (defined in Eq. (1)) was extended by Nachiappan and Anantharaman [21] from OEE, and was fully recognized and applied in the industry [22,23]. ...
Full-text available
Article
Maintenance task prioritization is essential for a production system, especially in the case of limited available resources. In this paper, the overall line efficiency (OLE), which comprehensively evaluates production line productivity and processability, is adopted as the objective to develop maintenance prioritization. An OLE centred maintenance prioritization policy is proposed, which assigns priorities according to the influences of the specific characteristics of equipment operational reliability on OLE. In order to correctly allocate limited maintenance resources to truly critical equipment and achieve good maintenance effect, equipment influences on OLE are analysed from the level of the specific performances of their operational reliability, i.e. equipment mean time between stops (mtbs), mean time to resume from stops (mttr) and buffer size N. Considering the interactions between these parameters, a surrogate model-extended Fourier amplitude sensitivity test (SM-EFAST) process is integrated to accurately and rapidly analyse the total effects of them on OLE. Finally, a case study has been carried out to verify the proposed OLE centred maintenance prioritization method, and higher OLE and lower total maintenance cost were achieved.
... Due to the comprehensive evaluation of system overall efficiency, OLE is fully recognized and applied in industry [20,21]. It was proposed by Nachiappan and Anantharaman [22], and defined as: line availability line production quality performance efficiency ...
Full-text available
Preprint
Maintenance task prioritisation is essential for production system, especially when available resources are limited. Related studies take either system safety or productivity as the objective of system maintenance prioritisation. While since improper system maintenance can make bad equipment cooperation thus induce unstable manufacturing process and big equipment operation time loss, system process ability is also necessary to be improved in production line maintenance. Therefore, the overall line efficiency (OLE) which comprehensively evaluates production line productivity and process ability is adopted as the objective to develop maintenance prioritisation in this study. Specifically, an OLE centred maintenance prioritisation policy which assigns priorities according to the influences of the specific characteristics of equipment operational reliability on OLE is proposed: To correctly allocate limited maintenance resources to truly critical equipment and achieve good maintenance effect, equipment influences on OLE are analysed from the level of the specific performances of their operational reliability: equipment mean time between stops (mtbs), mean time to resume from stops (mttr) and buffer size N. Considering the interactions between these parameters, a surrogate modelextended Fourier amplitude sensitivity test (SM-EFAST) process is integrated to accurately and rapidly analyse the total effects of them on OLE. In the end, an application case adopting the proposed OLE centred maintenance prioritisation method achieved higher OLE and lower total maintenance cost than two current popular maintenance prioritisation methods.
... It is applied as part of maintenance management and in comparison with other similar systems (Tonetto et al., 2013) which improve maintenance efficiency and lower the costs (Carazas & De Souza, 2009). Calculating availability from the performance evaluation approach is also utilized in the evaluation of theTPM philosophy implementationprogressas part of complex indexes,for example, the OEE (Samat et al., 2011;Laurenţiu, 2012;Jain et al., 2015), theOTE (Muthiah & Huang, 2007); the OLE (Nachiappan & Anantharaman, 2006), and the TEEP (Zandieh et al., 2012). Mohammadi et al. (2015), state that there are contradictions regarding the scope of time interval in the analysis, as some specialists use the calendar and others the required time (referred to outlined time).The analyzed time can be expressed considering the time out of service or unavailable, which means the possibility to express mathematically the operational availability in five different ways.If we take into account that in the case of the transport fleets, the obtainment of time out of service is easier than the available time and that the required time is equal to the calendar when the fleets operate in a continuous regime, then it is more appropriate to use the mathematical expression that relates time out of service and the required time.This is the way used by Tonetto et al. (2013) to make technical analysis with different vehicular compositions for wood transportation. ...
... In the product-based production layout, three monitoring systems have been identified which are overall line effectiveness (OLE), FPY and DPMO. According to Nachiappan and Anantharaman (2006), through their study of comparing the performance of the various continuous product line manufacturing-based industries, OLE can be considered as a continuous product line manufacturing system. The FPY is concerned about the productivity and on-time customer delivery according to the flow of Enhancement of the efficiency of internal supply chain production system 337 the connected production system (Antony et al., 2012), while DPMO is a measure of the defect for the overall production system, beginning from the first assembly process until the end of the process (Ravichandran, 2007) and it is more towards measuring the quality of the product-based production system as it involves with entire connected processes. ...
... According to Liao, Chen, and Wu (2008), the idea behind this may be to build customers' trust by providing consumers with all the goods they require, under one roof and thereby saving time and cost. Hypermarkets therefore provide consumers with comprehensive product options which consequently earn the hypermarkets the customer -even if it does not result in immediate sale (Glen, 2003;Nachiappan & Anantharaman, 2006;Roberts & McEvily, 2005). Deducing from the aforementioned arguments, this study postulate that: H2: Hypermarket size has a positive influence on customer perceived trust in hypermarket Perceived hypermarket reputation and perceived customer trust: As a collective determinant of dependability (trustworthiness), reputation can be a premise for customers' trust in a hypermarket. ...
Full-text available
Article
While a remarkable increase in research focusing customer purchase intentions in the retailing industry is noticeable, there is a dearth of studies that have investigated the influence of hypermarket size on customer perceived hypermarket reputation, trust in hypermarket and customer willingness to purchase in the African retailing context. This study used a sample 151 consumers in the Vanderbijlpark town in South Africa to examine these relationships. The results indicate that the proposed five hypotheses are positively supported in a significant way. A discussion of the academic and managerial implications of the results is provided and future research directions are suggested.
... However, the OEE tool is best only for an individual operation/machine but it is not suitable when consider production line or series of machines process. Therefore, Nachiapan suggested an alternative metric named overall line effectiveness (OLE) to evaluate the efficiency of manufacturing system having a continuous product flow (Nachiappan and Anantharaman, 2006). However, later on Marcello Braglia modified OLE as overall equipment effectiveness of manufacturing line (OEEML) (Braglia et al., 2008). ...
... Some renowned researchers suggested outcomes for this deficiency. Nachiappan and Anantharaman (2006) stated that it is vital to consider an appropriate metric strategy to take formulated decisions to manage processing houses. Wudhikarn (2016) said that the accuracy and precision of performance measurement are essential to reach the business goals in any industry. ...
Article
The swift changes in engineering and increasing technological concerns for the environment have pushed many industries to adjust their operations. The scope of study is to cover the critical and significant barriers in implementation of Overall Equipment Effectiveness (OEE) through Interpretive Structural Modelling (ISM). Based on a common proverb ‘If we can’t measure it, we can’t improve it”, ISM is the competent approach to measure the significance of its various variables (barriers), their hierarchy and interdependency on each other. The barriers affecting OEE implementation may vary as per industrial sectors, but the goal would remain the same, i.e. to surge performance. Fourteen critical barriers of OEE implementation were analysed from a Systematic Literature Review (SLR) which are described in this paper along with the directions and guidelines of industrial and academic experts. In addition, ISM model is proposed for the key barriers. It is necessary to measure the effectiveness of performance variables to gain overall efficiency. Interpretive Structural Modeling (ISM) may be used to understand the relationship between barriers and to develop a realistic model. The effective implementation of OEE involves extensive logical skills. It is therefore a time-consuming and brain-storming process. Once the behavioural pattern of barriers is determined through ISM, it will help the maintenance managers to predict the issues related to equipment’s maintenance and efficiency over a specific phase of time.
... When it comes to the original OEE, several research have addressed this issue by extending the OEE to measure the effectiveness and efficiency of the whole production line (OEEML) [5], [20], [21]. However, the literature does not address both issues at the same time, the singularity and considering customers' demand issues. ...
Full-text available
Article
Overall Equipment Effectiveness (OEE) is a widely accepted key performance indicator (KPI) to measure the effectiveness of total productive maintenance (TPM). However, OEE does not take customers’ demands into consideration and may encourage overproduction. Fitness to Takt Time (FiTT) is a modified OEE that measures not only the equipment’s effectiveness but also its fitness to the ideal production rate based on customers’ demand. However, just like OEE, FiTT measures the fitness of a single machine on the production line. To extend our analysis of the entire production system, this paper aims to provide a method in which the FiTT metric is expanded to measure the fitness of the entire production line. In this method, a similar approach to measure OEE for a manufacturing line (OEEML) is considered. The proposed method successfully captured the manufacturing line’s effectiveness and fitness to meet customers’ demands. An example is provided to illustrate the importance of the proposed method of uncovering improvement opportunities and encouraging continuous improvement towards optional excellence.
... However, when it is necessary to score the entire factory's production or a single entire line, different modified versions of the OEE should be applied. The OTE [21] or OFE [25] can be used to score the factory's production and the OLE [22] can measure a production line. However, due to the variety of the industry layouts, putting the equations into practice could be quite complicated then its calculation formula often is customized to meet particular production requirements [23]. ...
... With the strong support from Chinese government to manufacturing industry and the urgent need of its own industrial upgrading, China desperately needs to establish a mature, sophisticated, efficient and advanced equipment management system based on its own national conditions and the trend of global intelligence. For the cost of equipment maintenance has gradually become a key factor that affects manufacturing enterprises profit, it can be seen that an enterprise can improve efficiency and cost competitiveness of manufacturers depending largely on a series of decisions about equipment management (Nachiappan and Anantharam 2006;Adebanjo, Teh, and Ahmed 2018). ...
Full-text available
Article
Equipment management capability is crucial to the transformation and upgrading of manufacturing enterprises. Past studies ignore its significance and have not assessed manufacturing performance from the perspective of capability maturity yet and still want evidence from empirical test. For addressing this research gap, we combine research methods of qualitative and quantitative to define equipment management capability for exploring and verifying the relationship between equipment management capability maturity and manufacturing performance. Results of this study from 136 valid questionnaires indicate that capabilities of strategy, personnel and IT infrastructure have direct positive effects on manufacturing performance. Among them, equipment management strategy takes organizational & process and IT infrastructure as mediating variables, which have indirect positive influence on manufacturing performance. Meanwhile, organizational & process takes IT infrastructure as mediating variable which has indirect positive influence on manufacturing performance. Our work opens the black box of equipment management capability system and provides theoretical guidance for improving manufacturing performance.
... This has led to the development of new terminologies like Production Equipment Effectiveness -PEE (Raouf, 1994), Total Equipment Effectiveness Performance -TEEP (Ivančić, 1998), Overall Fab Effectiveness -OFE (Oechsner et al., 2003;Scott & Pisa, 1998), Overall Throughput Effectiveness -OTE (Huang et al., 2002), Overall Line Effectiveness -OLE (Nachiappan & Anantharaman, 2005), Overall Equipment Effectiveness of a Manufacturing Line -OEEML (Braglia, Frosolini, & Zammori, 2007), Overall Asset Effectiveness -OAE, Overall Production Effectiveness -OPE (Pintelon & Muchiri, 2008), Machining Equipment Effectiveness -MEE (Jaregui Becker, Borst, & Van Der Veen, 2015), Overall environmental equipment effectiveness -OEEE (Domingo & Aguado, 2015), Sustainable Overall Throughput Effectiveness -SOTE (Duran, Capaldo, & Duran Acevedo, 2018), Overall Task Effectiveness -OTE (Braglia, Gabbrielli, & Marrazzini, 2019). Moreover, many users often faced difficulties in classify all the possible equipment states based on the time model proposed by Nakajima. ...
Article
The International Organization for Standardization ISO22400 standard has been published for the purpose of defining a set of key performance indicators (KPI) for manufacturing operations management, and provides two definitions of the Overall Equipment Effectiveness (OEE) indicator. Despite OEE formulation has been well established for thirty years now, the ISO22400 standard versions seem to diverge one from the other and from the original Nakajima’s TPM formulation. Moreover, the standard appears to be incomplete, and many relationships among the terms and the definitions are imprecise. In this paper the authors report on the analysis of the ISO22400 OEE indicators and propose a classification of equipment states which can grant the standard consistency with the established OEE expression. Hence, the paper provides the reader with all the indispensable piece of information to practically handle this important KPI without ambiguity.
... However, the OEE tool is best only for an individual operation/machine but it is not suitable when consider production line or series of machines process. Therefore, Nachiapan suggested an alternative metric named overall line effectiveness (OLE) to evaluate the efficiency of manufacturing system having a continuous product flow (Nachiappan and Anantharaman, 2006). However, later on Marcello Braglia modified OLE as overall equipment effectiveness of manufacturing line (OEEML) (Braglia et al., 2008). ...
Article
An effective measurement tool and strategic plan is beneficent for the organizations to face the outsiders’ competitors and to achieve the required demands of the global market. This research is based on a survey of five weeks study of beverage bottling industry. The key metric OEE is usually used to measure the performance of individual machine. However, OEE is not sufficient to enhance the performance of manufacturing line as a whole. To overcome this, a performance measurement metric namely, overall equipment effectiveness of manufacturing line (OEEML) implemented for measuring the actual performance of the beverage bottling production line (BBPL). The final results of the BBPL improved with a high percentage 14% of OEEML increased which results to increase the output productivity of the beverage bottling production line and reduction in per unit cost. In the end OEEML is implemented as a strategy, i.e., a concept it works best for continuous improvements for the overall BBPL.
... The related metric calculation allows bottleneck detection and identifying hidden capacity [23][24][25]. In addition, viewing the cost structure for capacity planning, Nachiappan and Anantharaman [26] proposed overall line effectiveness (OLE) based on the fundamental structure of OEE. Composed of line availability (LA) and line production quality efficiency (LPQP) which enables the determination of ineffective production process. ...
Full-text available
Conference Paper
Moore’s Law indicates that the amount of transistors which can be accommodated on integrated circuit (IC) double every two years in semiconductor manufacturing industry. As a capital intensive and competitive industry, supply chains in the semiconductor industry feature high structural complexity and high demand uncertainty. In order to enhance the effectiveness of the enterprise, total resource management (TRM) for semiconductor industry is increasingly crucial. Past studies focused on measuring the effectiveness of either demand fulfillment or capacity utilization, but little researches have done for investigating the inter-coordination on both aspects. This paper proposes overall supply chain effectiveness (OSCE) for the incorporation of demand planning and capacity portfolio based on PDCCCR framework to evaluate the effectiveness of semiconductor supply chain industry. The proposed index is implemented in an empirical study based on a simulation model for semiconductor backend production. Finally, the paper concludes with the discussion of future research directions.
... Manufacturers may adapt the standard to their own processes in order to identify specific problems or to obtain insights into improvement opportunities, see for instance (Nachiappan and Anantharaman, 2006). Seiichi Nakajima (1988) identifies the optimum OEE figures and asserts that under ideal conditions, organization should have Availability > 90%; P erf ormance > 95%; Quality > 99% resulting to an OEE > 85% for world-class level. ...
Article
The Overall Equipment Effectiveness is widely used to monitor the performance of manufacturing systems. It allows to compare the actual performance of a piece of equipment, internally or with external benchmarks. The ultimate goal is to use it proactively in a control loop. The paper looks for providing a dependency model linking the decision makers’ actions and the Overall Equipment Effectiveness. The obtained model is a Bayesian network which allows to measure the impact of various control strategies and to select the most appropriate ones. The approach is illustrated through an industrial case.
Article
Nakajima defined the ‘Overall Equipment Effectiveness’ (OEE) indicator to measure the performance losses of a company. Since then, variants have been proposed by authors and standardisation bodies to make it more suitable for new production contexts. OEE allows measuring performance losses and is used to determine improvement projects. The use of different OEE measurement systems can lead to different improvement projects; i.e. the use of inappropriate measurement systems leads to the resolution of wrong problems. It is, therefore, necessary to understand OEE and its variants to choose the most appropriate measurement system and thus focus improvement efforts on the least efficient processes. This need is initially specified by a French-Spanish industrial group. However, to our knowledge, there is no comparative study of the different OEE measurement systems. Our review of the scientific literature and international standards revealed four measurement systems widely used and commonly applied in industry, namely, Nakajima, ISO, SEMI and AFNOR. We analysed these different measurement systems in depth, proposed a reference taxonomy of loss families, and compared them all. These measurement systems were then applied to the case of one of the industrial group’s plants to determine their adequacy and compare them with the existing OEE measurement system of the industrial group. An OEE calculator offering these different measuring systems has been programmed and installed in the factory. The main results of this research work are as follows. First, a reference taxonomy of loss families is proposed. The effective comparison carried out for the four widely used measurement systems is the second tangible result. Finally, this study made it possible to determine and validate the main characteristics to be taken into account in the final choice of an OEE measurement system in a real case.
Chapter
This chapter presents a structural equation model integrated by four latent variables that are related using six hypotheses. The independent variables are Cell layout (CLA), Total productive maintenance (TPM), and Single-minute exchange of die (SMED), and the dependent variable is Inventory minimization (INMI). The model is evaluated using the partial least squares technique with information from 228 responses to a questionnaire applied to the manufacturing industry. The direct effects, the sum of indirect and total effects are analyzed. The results indicate that TPM and SMED have the most significant explanatory power on INMI since they allow the flow of materials throughout the production process.KeywordsSEMCellular layoutTPMSMEDInventory minimization
Chapter
Product quality is one of the concerns of manufacturing managers, and the question they frequently ask themselves is, what should they do to guarantee it and achieve customer satisfaction? This paper presents a structural equation model (SEM), where Quality control (QUC) is the dependent variable, and it is assumed that Pull system (PUS), Small-lot production (SLP), and Uniform production level (UPL) can explain it. The variables are related using six hypotheses validated using the partial least squares technique in WarpPLS v.7.0 software, where a sensitivity analysis based on conditional probabilities is also reported. The results indicate that PUS is a lean manufacturing practice that has a high relationship with SLP. At the same time, SLP is associated with UPL, and finally, the latter is associated with QUC.KeywordsSEMPull systemSmall-lot productionUniform production levelQuality control
Article
The swift changes in engineering and increasing technological concerns for the environment have pushed many industries to adjust their operations. The scope of study is to cover the critical and significant barriers in implementation of Overall Equipment Effectiveness (OEE) through Interpretive Structural Modelling (ISM). Based on a common proverb ‘If we can’t measure it, we can’t improve it”, ISM is the competent approach to measure the significance of its various variables (barriers), their hierarchy and interdependency on each other. The barriers affecting OEE implementation may vary as per industrial sectors, but the goal would remain the same, i.e. to surge performance. Fourteen critical barriers of OEE implementation were analysed from a Systematic Literature Review (SLR) which are described in this paper along with the directions and guidelines of industrial and academic experts. In addition, ISM model is proposed for the key barriers. It is necessary to measure the effectiveness of performance variables to gain overall efficiency. Interpretive Structural Modeling (ISM) may be used to understand the relationship between barriers and to develop a realistic model. The effective implementation of OEE involves extensive logical skills. It is therefore a time-consuming and brain-storming process. Once the behavioural pattern of barriers is determined through ISM, it will help the maintenance managers to predict the issues related to equipment’s maintenance and efficiency over a specific phase of time.
Chapter
In almost all cases, automotive companies and automotive suppliers monitor the efficiency of their production and measure it with different metrics. Based on predefined Key Performance Indicators (KPI’s), the production results of manufacturers show generally definite trends. Higher production efficiency induces higher financial results. Companies typically use Overall Equipment Effectiveness (OEE) to measure and evaluate their production as a gold standard and best practice. In production 100% score of OEE means only good parts (which are accepted by the customer), as fast as possible (based on production plan), without stoppage time. This article is looking for answer to the question how to reach maximum effectiveness and under what circumstances this can be overcome at a hybrid assembly line. Firstly, a literature review demonstrates its scientific relevance. Secondly, an example from the automotive industry illustrates how to perform close to 100% on a semi-automatic assembly line and in which cases it can be exceeded. OEE components, as availability, performance and quality are examined in detail to get excellent percentage. This paper highlights that if the operator performs at the gearbox semi-automatic line above the expected cycle time and with stable entire logistics process, the OEE value may be higher than 100%.
Book
The five-volume set IFIP AICT 630, 631, 632, 633, and 634 constitutes the refereed proceedings of the International IFIP WG 5.7 Conference on Advances in Production Management Systems, APMS 2021, held in Nantes, France, in September 2021.* The 378 papers presented were carefully reviewed and selected from 529 submissions. They discuss artificial intelligence techniques, decision aid and new and renewed paradigms for sustainable and resilient production systems at four-wall factory and value chain levels. The papers are organized in the following topical sections: Part I: artificial intelligence based optimization techniques for demand-driven manufacturing; hybrid approaches for production planning and scheduling; intelligent systems for manufacturing planning and control in the industry 4.0; learning and robust decision support systems for agile manufacturing environments; low-code and model-driven engineering for production system; meta-heuristics and optimization techniques for energy-oriented manufacturing systems; metaheuristics for production systems; modern analytics and new AI-based smart techniques for replenishment and production planning under uncertainty; system identification for manufacturing control applications; and the future of lean thinking and practice Part II: digital transformation of SME manufacturers: the crucial role of standard; digital transformations towards supply chain resiliency; engineering of smart-product-service-systems of the future; lean and Six Sigma in services healthcare; new trends and challenges in reconfigurable, flexible or agile production system; production management in food supply chains; and sustainability in production planning and lot-sizing Part III: autonomous robots in delivery logistics; digital transformation approaches in production management; finance-driven supply chain; gastronomic service system design; modern scheduling and applications in industry 4.0; recent advances in sustainable manufacturing; regular session: green production and circularity concepts; regular session: improvement models and methods for green and innovative systems; regular session: supply chain and routing management; regular session: robotics and human aspects; regular session: classification and data management methods; smart supply chain and production in society 5.0 era; and supply chain risk management under coronavirus Part IV: AI for resilience in global supply chain networks in the context of pandemic disruptions; blockchain in the operations and supply chain management; data-based services as key enablers for smart products, manufacturing and assembly; data-driven methods for supply chain optimization; digital twins based on systems engineering and semantic modeling; digital twins in companies first developments and future challenges; human-centered artificial intelligence in smart manufacturing for the operator 4.0; operations management in engineer-to-order manufacturing; product and asset life cycle management for smart and sustainable manufacturing systems; robotics technologies for control, smart manufacturing and logistics; serious games analytics: improving games and learning support; smart and sustainable production and supply chains; smart methods and techniques for sustainable supply chain management; the new digital lean manufacturing paradigm; and the role of emerging technologies in disaster relief operations: lessons from COVID-19 Part V: data-driven platforms and applications in production and logistics: digital twins and AI for sustainability; regular session: new approaches for routing problem solving; regular session: improvement of design and operation of manufacturing systems; regular session: crossdock and transportation issues; regular session: maintenance improvement and lifecycle management; regular session: additive manufacturing and mass customization; regular session: frameworks and conceptual modelling for systems and services efficiency; regular session: optimization of production and transportation systems; regular session: optimization of supply chain agility and reconfigurability; regular session: advanced modelling approaches; regular session: simulation and optimization of systems performances; regular session: AI-based approaches for quality and performance improvement of production systems; and regular session: risk and performance management of supply chains *The conference was held online.
Chapter
The discipline of Asset Management (AM), which focuses on the management of physical assets in an integrated and holistic way along their life cycle, can be adopted by companies to promote sustainability since it enhances asset reliability and availability for the whole duration of its usage. Within the manufacturing industry, a relevant AM-related performance indicator is the Overall Equipment Effectiveness (OEE), which measures the efficiency of equipment. However, traditionally OEE measures the performance of individual equipment only, while neglecting the system perspective, which is core in AM. Only few contributions propose an extension towards a system-level performance indicator. After the OEE-related system-level indicators from the scientific literature are reviewed, an application of one of them in an industrial case is presented, selected as the indicator best fitting the characteristics of the industrial case itself, which is a disconnected flow manufacturing line. The application of the system-level indicator allows comparing it with the traditional OEE. Results show that a system approach better supports AM since the information carried out by the indicator is more complete and adherent with the actual asset and system characteristics. In turn, the system-level perspective is assumed just as a first step towards a holistic performance improvement as it is required by AM. A step forward to fulfill the sustainable performance is the integration of measurements of other sustainability-related impacts leading to effective asset-related decisions.
Chapter
The model proposed in this article brings together the application bases of a Total Productive Maintenance (TPM) system as the beginning towards Lean transformation for a mining company. It takes as reference models and success stories that were previously implemented and is adapted to guarantee the success of its application. Likewise, it includes an optimal indicator to evaluate the efficiency of mining equipment, the Mining Production Index - MPI, which is an adaptation of the OEE to the mining context. This methodology can be applied to other mining environments since it is totally flexible in each of its steps. A case study is carried out to evaluate the potential application of the proposal, attacking each of the problems found in order to increase the efficiency of the mining equipment. Inside, the initial conditions of the mine were determined and how the proposed methodology could help improve it was evaluated.
Full-text available
Chapter
Should there be an understanding that rigor in analysis must be out-of-bounds for Lean initiatives? Will this rigor not facilitate a benchmarking of Lean initiatives? Why not a Lean initiative cause-consequence assessment not performed for building future fault tolerance? The effectiveness of a company’s strategy is critical to its success or failure. Lean strategy seems to be claimed as a widely recognized factor for business success and competitive advantage. However, empirical evidences do not promote the idea that Lean has delivered results every time. Study results indicate that success or failure of lean initiatives strongly depends on how companies approach it and on whether company has created their own curated philosophy towards Lean. Then, success is not dependent alone on a strategy, but on how daily operations are aligned to strategy. This chapter aims to address the above questions and a greater number of questions that we experience on a day-to-day basis with regard to Lean applications in the real world. Chapter Learning Objectives: Understanding Lean, Lean failure modes, and Lean initiative precautions.
Chapter
The engineering sectors have a considerable contribution toward a nation’s economy. These industries are amid the principle-focused industries around the globe. Hence, appropriate utilization of available resources as well as equipment is a high priority in task list of every industry. Since many of these industries are dealing with hurdles to keep all the activities, operations, and synchronized use of equipment with a considerable reduction of time losses. These time losses (so called Six Big Losses) have a set of analyzing factors, which affect the overall performance. Six Big Losses (SBL) are now becoming the real cause of focus to attain world class manufacturing standards in association with strategic tool such as OEE (Overall Equipment Effectiveness). To cover the wide-ranging and dynamic time loss analysis, the corrective measure is OEE. The aim of implementation of OEE is to enhance and acquire the equipment’s effectiveness by removing all the potential losses of the industry. This paper discloses the importance of the extensive literature for analysis of these losses systematically. This study also covers a powerful organized strategy, i.e., OEE and the actual time losses in the industries to bring transformation in the attitude of management, managers and workers. This paper tunes up the literature related to SBL to find the potential errors through optimized approach to improve decision-making.
Article
In the global market scenario, sustaining in the ecumenical organization concentrates on their manufacturing and system. For this, the manufacturing industries were adopted to various world-class manufacturing implements in their organization. In which Total Productive Maintenance (TPM) implement plays a significant role. The implementation of TPM has been understood well by its effectiveness evaluation. The evaluation has also aimed to assess the current manufacturing performance status and execute amendments for further magnification. The evaluation has been carried out by sundry researchers in different indices for their desiderata and requisites under the substructure of Overall Equipment Effectiveness (OEE). The index has been developed to evaluate individual equipment and elongate it for the whole manufacturing system. A detailed literature review has been carried out and summarized with deference to the past three decenniums; the review includes the researcher's highlights or contribution with the difficulties associated with evaluating it in manufacturing industries.
Full-text available
Article
Futura Energy Nusantara is the first manufacturer of lithium-ion battery cell production in Indonesia. Because highly demand, the company must fill the order and increase production Productivity is one of the most fundamental and important determinants of system production. Productivity measurement help identify the problems and find solutions to improve system performance. OEE (Overall Equipment Effectiveness) is one method used for effective time manufacturing systems related to the existence of equipment in the production system. The Element of OEE is Availability Rate (A), Performance Efficiency (P), and Quality Rate (Q). The study aims to test through the simulation production system in Lithium-Ion Batteries (LIB), by considering factor OEE, with discrete event simulation system. As a result, this study is a model to applicable, for the company to improve Productivity
Article
The manufacturing industries are among the focused industries of any country according to their contribution to nations’ economies. Therefore, the appropriate utilization of all manufacturing/processing equipment is a high priority. The problem is the complexity to keep all the activities, operations, and use of equipment in synchronization with the reduction of all possible losses and defects. Such losses are becoming the real cause of the investment in the maintenance section of the industrial units. The different versions of established concepts define ‘Maintenance’ as ‘repair of the equipment,’ but this concept covers old and limited dimensions. Therefore, this notion relates the maintenance strategies only as a function of preventive maintenance, predictive maintenance and corrective maintenance. At the basic level of OEE implementation, various process industries of the country are yet unaware of the potential benefits of this strategic tool. The quality and performance-enhancing approaches are being developed based on time management and production loss analysis to get the optimum output from available resources. This study is to mark all the critical strategies associated with sustainable performance and to trace all possible drawbacks while measuring effectiveness through OEE (Overall Equipment Effectiveness). OEE is a tool of Total Productive Maintenance (TPM) without whom the effective utilization of resources is a difficult task. OEE supports the entire perspective on effectiveness uniquely and logically but also improves the life span of the machinery through improvements and monitoring in the maintenance operations and activities. This paper aims to identify the role of different strategies associated with OEE for unceasable production and optimum use of resources. This paper arranges the literature in an organized way and assists to find promising decisions to implement OEE with integrated concepts.
Article
In today’s global competitive manufacturing environment for survival of any firm it is necessary to provide quality goods to its customers at competitive prices. It is possible only if the firm is running at its highest level of productivity. That is why the firms are focused on productivity improvement tasks. The Total Productive Maintenance (TPM) is a key tool to improve productivity exercising its various pillars. Whole hearted implementation of training and development pillar of Total Productive Maintenance is of utmost importance in improving skills of the operators and other employee of the company. Measuring performance is vital for its improvement. The objective of this paper is to identify the performance measurement metrics to measure and verify effect of the pillar. A conceptual framework has also been presented that outlines the selection and effective application of performance measurement metrics aligning manufacturing performance and maintenance objective through effective training and skill development.
Full-text available
Article
The globalization of the Indian economy has thrown a great challenge to the Indian Industries in respect of productivity, quality, cost, delivery etc,.as a sequel to this; the survival of many industries is at shake. Total productive maintenance (TPM) is an excellent tool to provide business houses the competitive advantage. The present paper has dealt with a as study taken from one of the reputed engineering company, where TPM is being implemented to vertical boring machine to improve its performance, reduce breakdown time. After implementation of TPM breakdown time is reduced from 56 hours to 3 hours per month and increase OEE from 45% to 79%. Now company has taken brave decision of launching TPM all over the plant, which ultimately show the efficacy of TPM as a potent tool for enhancing the overall performance in business. Key words; TPM, Kaizen, OEE
Full-text available
Article
Traditional productivity metrics, such as throughput and utilization rate, are not very helpful for identifying the underlying problems and opportunities for productivity improvement in a manufacturing system. In this paper, a systematic methodology is presented for productivity measurement and analysis at the factory level. Metrics of Overall Equipment Effectiveness (OEE) and Overall Throughput Effectiveness (OTE) are introduced and developed, respectively, for rigorous and quantitative measurement of equipment and system productivity. These metrics are integrated with computer simulation to facilitate rapid analysis of equipment and manufacturing system productivity, and the investigation of productivity improvement opportunities. The results of this research make possible the representation of factory level productivity or overall factory effectiveness by OTE, and the use of OTE for quantitative benchmarking and comparison of the productivity of various factories. A real-world manufacturing case study is reported to demonstrate how to employ these techniques to improve manufacturing productivity.
Full-text available
Article
The paper identifies six requirements: four critical dimensions (what to measure) and two characteristics (how to measure) of an overall manufacturing performance measurement system. The overall equipment effectiveness (OEE) measure in such a system is assessed against these ideal requirements. The current measurement systems, and the potential of OEE, of three manufacturing organisations are evaluated with the dimensions and characteristics as comparative data. A common weakness of the systems was that they did not measure flow orientation or external effectiveness to any great extent. Another weakness was a high degree of complexity and lack of continuous improvement. Field experiments in the studied organisations showed that use of OEE in combination with an open and decentralised organisation design could improve several of those weaknesses.
Article
Presents a practical analysis of operational performance measurement at Airbags International Ltd (AIL), a supplier of airbag safety devices to the automotive industry. First, the primary measure of overall equipment effectiveness (OEE) is described. Its implementation and use within the operational environment of AIL is then described and analysed. Finally, presents the potential benefits of developing OEE as an operational measure and contrasts AIL’s performance with other applications of OEE found with the research literature.
Article
The accurate estimation of equipment utilization is very important in capital-intensive industry since the identification and analysis of hidden time losses are initiated from these estimates. In this paper, a new loss classification scheme for computing the overall equipment effectiveness (OEE) is presented for capital-intensive industry. Based on the presented loss classification scheme, a new interpretation for OEE including state analysis, relative loss analysis, lost unit analysis and product unit analysis is attempted. Presents a methodology for constructing a data collection system and developing the total productivity improvement visibility system to implement the proposed OEE and related analyses.
The role that effective maintenance management plays in contributing to overall organizational productivity has received increased attention. Presents the development of a situational maintenance model that may be used to analyse and design the elements of a maintenance system. The situational approach to maintenance builds on contingency theory and considers both internal and external corporate dynamics. Using ideas from total productive maintenance (TPM), discusses how this model may be used to link corporate goals with maintenance policies. Defines design variables for maintenance systems that include the perspectives of individual behaviour, decision support systems, management systems and organizational structure, and corporate culture.
Article
Assessment of disturbance data from production equipment provides valuable information for improving productivity. To implement total productive maintenance (TPM) it is necessary to assess the magnitude of different types of production losses, in order to direct activities and allocate resources in an optimal way. In many enterprises, the focus is directed towards major time losses due to break-downs rather than minor losses in speed and time. A method for implementing data collection is to start gradually by a simple model and develop this to a combined model with computerised systems and manual recording. This gives both an exact assessment of the magnitude of the disturbance and a deeper understanding of the reason for losses. According to about 20 cases, the overall equipment effectiveness was only around 55 per cent. Further, it is quite clear that performance losses are the dominating ones. If the production process is new to the company it is hypothesised that overall equipment effectiveness will be lower than if the company is already familiar with production process.
Article
An enhanced approach for implementing total productive maintenance (TPM) in the manufacturing environment is discussed. The failure mode, effects, and critical analysis (FMECA) is an excellent tool that can be used in identifying system failures, failure modes and frequencies. The reliability centered maintenance (RCM) which is based primarily on the FMECA, can be used in developing a life-cycle oriented preventive maintenance programme. Finally, managers, engineers and technicians must be familiar with what is available and must utilize these tools.
Article
Personalization of products, mix variability and short time to market are the most important factors that have forced companies to a new form of organization during past years. A very common reply to this question is a lean organization based on flexibility of productive lines, reduction of storage and integration among company sections. In this context, quite differently from a traditional system, the maintenance function must work efficiently. Also the maintenance division must contribute to the success of the factory. Aims to introduce a methodology for a soft and tenable application of the principles of total productive maintenance (TPM) in Italian factories. The first step of the study is an explanation of the actual situation, usually based on traditional or on productive maintenance. After a brief introduction, focuses on TPM links with productive maintenance in order to suggest a method for TPM. Concludes with a real application of TPM in a big factory, with a description of a world leader in plant manufacturing for the ceramics industry.
Article
Manufacturing companies are under pressure to minimize production costs. Reducing downtime and minimizing maintenance costs are the traditional approach. Improving production equipment effectiveness as prescribed in total productive maintenance (TPM) is a relatively recent development. Outlines improving maintenance productivity through structural audit and improving production equipment effectiveness for discrete type production systems and continuous processes. Suggests continuous improvement of production equipment effectiveness by periodic evaluations.
Article
The semiconductor industry has gone through significant changes in the last decade. Competition has increased dramatically. Customers focus on product quality, product delivery time and cost of product. Because of these, a company should introduce a quality system to improve and increase both quality and productivity continuously. Total productive maintenance (TPM) is a methodology that aims to increase the availability of existing equipment hence reducing the need for further capital investment. Investment in human resources can further result in better hardware utilisation, higher product quality and reduced labour costs. The aim of the paper is to study the effectiveness and implementation of the TPM programme for an electronics manufacturing company. Through a case study of implementing TPM in an electronics manufacturing company, the practical aspects within and beyond basic TPM theory, difficulties in the adoption of TPM and the problems encountered during the implementation are discussed and analysed. Moreover, the critical success factors for achieving TPM are also included based on the practical results gained from the study. After the implementation of TPM model machine, both tangible and intangible benefits are shown to be obtained for equipment and employees respectively. The productivity of the model machine increased by 83%.
Article
The continuous improvement concepts such as total quality management, just-in-time and total productive maintenance have been widely recognized as a strategic weapon and successfully implemented in many organizations. In this paper, we focus on the application of total productive maintenance (TPM). A random effect non-linear regression model called the Time Constant Model was used to formulate a prediction model for the learning rate in terms of company size, sales, ISO 9000 certification and TPM award year. A two-stage analysis was employed to estimate the parameters. Using the approach of this study, one can determine the appropriate time for checking the performance of implementing total productive maintenance. By comparing the expected overall equipment effectiveness (OEE), one can improve the maintenance policy and monitor the progress of OEE.
Article
Remarkable improvements have occurred recently in the maintenance management of physical assets and productive systems, so that less wastages of energy and resources occur. The requirement for optimal preventive maintenance, using, for instance, just-in-time (JIT) and total quality-management (TQM) techniques, has given rise to what has been called the total productive-maintenance (TPM) approach. This study explores the ways in which Nigerian manufacturing industries can implement TPM as a strategy and culture for improving its performance and suggests self-auditing and bench-marking as desirable prerequisites before TPM implementation.
World class manufacturing and benchmarking - the philosophy of performance enhancement
  • S C Agarwal
TPM Activities in Denmark
  • B Bandgaard
  • H. Niels
Equipment effectiveness and six big losses
  • C Chowdhury
  • T K Mandal
TPM: a comprehensive tool for achieving excellence in operations system - a case study
  • D Das
Implementing TPM - a practical experience
  • K Kant
Consistency, accuracy lead to maximum OEE benefits
  • D Kotze
Analysis of OEE - a case study
  • N Muthukumar
  • R M Nachiappan
  • S M Kannan
  • K Sevagan
The buzz on benchmarking: compare your performance with the best to improve production and out cost”, Maintenance Technology, available at: www.maintenance resources
  • M C Queen
Monthly TPM Activities Presentation, Department of Milling unit
  • The Head
TVS Rubber Factory – TPM Implementation Manual
  • TPM Coordinators
Learning curve analysis in TPM
  • F.K Wang
  • W. Lee
Benchmarking and world-class manufacturing for competitive advantage
  • Ajay Pandit
  • K K Yunjing
Application of TPM to vertical boring machine
  • S B Barve
  • M S Birajdar
  • A S Bhongade
  • H Chaudhari
TPM activity pre audit report, Internal circulation copy
  • Tpm Chairman