In recent years due to increased competition between companies in the services sector, predict churn customer in order to retain customers is so important. The impact of brand loyalty and customer churn in an organization as well as the difficulty of attracting a new customer per lost customer is very painful for organizations. Obtaining a predictive model customer behaviour to plan for and deal with such cases, can be very helpful. Employee churn or loss of staff will be close to the customer churn, but the impact of losing a major customer for organization certainly will be more painful (because organization do not have physical sense to losing their employees) while the consequences of finding well employees instead of missed employees, As well as the cost of in-service training that should be given to new employees could be one of the issues that each organization would be sensitive to losing its human resources.

Today, small- and medium-sized enterprises (SMEs) play an important role in the economy of societies. Although environmental factors, such as COVID-19, as well as non-environmental factors, such as equipment failure, make these industries more vulnerable, they can be minimized by better understanding the concerns and threats these industries face. Only a few SMEs have the capacity to implement the innovative manufacturing technologies of Industry 4.0. The system must be highly adaptable to any equipment, have low costs, avoid the need of doing complex integrations and setups, and have future reliability due to the rapid growth of technology. The goal of this study was to provide an overview of past articles (2010–2020), highlighting the major expectations, requirements, and challenges for SMEs regarding the implementation of predictive maintenance (PdM). The proposed solutions to meet these expectations, requirements, and challenges are discussed. In general, in this study, we attempted to overcome the challenges and limitations of using smart manufacturing—PdM, in particular—in small- and medium-sized enterprises by summarizing the solutions offered in different industries and with various conditions. Moreover, this literature review enables managers and stakeholders of organizations to find solutions from previous studies for a specific category, with consideration for their expectations and needs. This can be significantly helpful for small- and medium-sized organizations to save time due to time-consuming maintenance processes.

Incorrect sensor readings can cause serious problems in Wireless Sensor Networks (WSNs), potentially disrupting the operation of the entire system. As shown in the literature, they can arise from various reasons; therefore, addressing this issue has been a significant challenge for the scientific community over the past few decades. In this paper, we examine the applicability of seven distributed consensus gossip-based algorithms for sensor fusion (namely, the Randomized Gossip algorithm, the Geographic Gossip algorithm, three initial configurations of the Broadcast Gossip algorithm, the Push-Sum protocol, and the Push-Pull protocol) to compensate for incorrect data in WSNs. More specifically, we consider a scenario where the sensor-measured data (measured by a set of independent sensor nodes) are skewed due to Gaussian noise with a various standard deviation $\sigma$, resulting in discrepancies between the measured values and the true value of observed physical quantities. Subsequently, the aforementioned algorithms are employed to mitigate this skewness in order to improve the accuracy of the measured data. In this paper, WSNs are modeled as random geometric graphs with various connectivity, and the performance of the algorithms is evaluated using two metrics (specifically, the mean square error (MSE) and the number of sent messages required for an algorithm to be completed). Based on the presented results, it is identified that all the examined algorithms can significantly suppress incorrect sensor readings (MSE without sensor fusion = −0.42 dB if $\sigma$ = 1, and MSE without sensor fusion = 14.05 dB if $\sigma$ = 5), and the best performance is achieved by PS in dense graphs and by GG in sparse graphs (both algorithms achieve the maximum precision MSE = −24.87 dB if $\sigma$ = 1 and MSE = −21.02 dB if $\sigma$ = 5). Additionally, the performance of the analyzed distributed consensus gossip algorithms is compared to the best deterministic consensus algorithm applied for the same purpose.

Many managers are looking to engage employees and customers because they know this is one of the key factors in running a successful business. In the first place, this will reduce the customers and employees churn and will further increase their loyalty and satisfaction to the organization. Using gamification to engage stakeholders is an interesting topic for many organizations, but the lack of a clear procedure for applying these methods has led to not achieving the expected results.In this study, we will introduce a new technique for designing gamification in servicing companies in which customers and employees interact with each other also examine the principles of setting up a game site based on statistical computing and data mining algorithms.

As the penetration of using clean energy in government plans and companies is rising, many researchers are seeking the influence of multiple factors on the processes leading to producing renewable energy. Electricity via photovoltaic (PV) cells, quickly became popular in all countries due to fewer restrictions compared to other energies. In this study, we compared different machine learning methods based on the classification and prediction of solar energy output. by analyzing a specific case study in Slovakia, Finally, this model was implementedin the RapidMiner platform and the effective factors in predicting by comparing evaluation were identified.