The advent of globalization and outsourcing of products and services has resulted in more networked, highly dependent firms in supply chains,so supply chains’,vulnerability to several risks such as equipment breakdown is increased. Although optimizing maintenance supplier selection has a vital role in supply chain performance, it has not received enough attention in the literature. Due to the effect of maintenance strategies and supplier selection approaches, in this paper, three scenarios are considered based on various strategies as follows: 1-Preventive Maintenance with single supplier selection (PMs), 2-Preventive Maintenance with multiple supplier selection (PMm), and 3-Condition Based Maintenance with multiple supplier selection (CBMm). A fuzzy goal programming approach is applied to make trade-offs between several objectives. The efficacy of the model is validated through numerical examples, and results are compared to investigate the effect of implementing each scenario. The results show that in situations that reducing unreliability cost has higher priority for a decision-maker, CBMm is more efficient than other scenarios.

In industries, Preventive Maintenance (PM) is not a new practice to minimise the sudden breakdown of production machine. PM will be performed at predetermine intervals to provide a balance between failure cost and component utilisation (aging). However, most of PM interval is based on experience or original equipment manufacturer (OEM) recommendations. Consequently, the benefits from PM are not fully obtained because the current machine state is not considered. The aim of this paper is to propose a maintenance management decision model for PM application. The main objective of the model is to revise the PM interval based on current machine state. The proposed model provides step-by-step procedure from data identification, classification, evaluation until determination of the revised PM interval.

Condition-based maintenance (CBM) is a well-known maintenance cost minimization strategy in which maintenance activities are performed based on the actual state of the system being maintained. The act of combining maintenance activities for different components is called opportunistic maintenance or maintenance clustering, which is known to be cost-effective, especially for multi-component systems with economic dependency. Every operating system is subject to gradual degradation which ultimately leads to system failure. Since each level of degradation can be represented by a state, every system can be modeled as a multi-state structure. The state of a system can be estimated through condition monitoring, albeit with uncertainty. The majority of studies in the field of maintenance planning are focused on preventive perfect maintenance operations such as replacement. But in practice, most of the maintenance operations are imperfect because of time, technology, and resource limitations. In this paper, we present a CBM clustering model that factors in uncertainty in alerting and lifetime distribution and considers the possibility of using the imperfect maintenance approach. This model is developed for a system with three levels of warning (Signal, Alert, Alarm), which combines inspections and condition monitoring to avoid unnecessary inspections and thereby achieve better cost-efficiency. Our analysis and results provide a general view of when and how to cluster maintenance activities to minimize maintenance costs and maximize system availability. Numerical investigations performed with MATLAB show that clustering CBM activities can result in as much as 80% cost saving compared to No clustering.

Over the past decades dental implant with 90-98% success rate has become an integral part of reconstructive dentistry. As the supporting tissues of the implant have different structure and circumstance with natural teeth like less vascularity and different collagen fibers arrangement and amount, dental implants are more susceptible to infection and bone resorption, thereafter an extensive and careful maintenance protocol should be followed. However, the attraction of patient to implant treatment is growing up, the maintenance protocol following treatment is ignored. This neglect or maybe lack of awareness can lead to peri-implantitis, bone resorption and finally implant failure. Although there are some risk factors for periimplantitis like: smoking, history of periodontitis, limited oral hygiene, systemic disease and poor plaque control. The awareness and periodic recall of the patient by dentist (every three months in the first year and every six months thereafter) play a great role in prevention of implant failure. This study is aimed to review the maintenance protocol which is divided into two main category called professional (sonic and ultra-sonic scaler, plastic or Teflon coated curettes, oral irrigation and occlusal adjustment) and home care (manual tooth brush, mechanical tooth brush, dental floss, interproximal brush and antimicrobial). An electronic search for article in English language literature from the past 5 years about implant maintenance was performed using a systemic search process. Nowadays implant treatment has become a main part of dentistry which involves different specialties from periodontology to surgery and prosthesis, the point here is the behavior of the patient after treatment following the principle to keep the implant in a best situation to avoid failure and additional costs.