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1. Introduction

Recently, the manufacturing systems domain underwent a paradigm shift by introducing several key enabling technologies as a requirement of Industry 4.0 [1]. Keeping in mind clients’ customized requirements and global manufacturers’ personalized production, the current production and process capabilities need to be transformed. For example, recent requirements such as shorter product life cycles, high production rates, jobs complexity, quality products, and cost-effectiveness are the most significant factors for any manufacturing industry [2]. Considering all the foregoing requirements, and, in addition, according to with the current market demand and society requests, there is a need to enhance the system’s capabilities by maintaining it under control from system breakdowns and several external forces that have not been considered as one of the highest priority in the past decade. To accomplish these challenges, there is a need for high machine availability, flexibility, configurability, and accessibility of manufacturing processes, as mentioned in [3-9]), along with another interesting contribution for emphasizing the necessity of increasing the level of flexibility of manufacturing systems, which can be seen in https://publications.muet.edu.pk/index.php/muetrj. However, various manufacturing systems available to fulfil the above-mentioned requirements have costs affairs and high maintenance. In this review paper, we introduced a special kind of configuration: i.e., flexible unit systems (FUS) with one degree of flexibility, two degrees of flexibility, semi flexibility, and highly flexible configurations, where the reconfiguration and upgradation of unit (machine) systems are easily achieved [10,11].

The common factors from different studies that affect FUS are identified as degradation rate, residual life distribution, workload strategy, upgradation, and predictive maintenance. To improve the health status of the system and to make the manufacturing functions effective and efficient, system-level health monitoring is new thinking to which nowadays researchers are paying attention. Therefore, the degradation rate at the system level is of the highest priority. Studies have shown that manufacturing systems are subjected to degradation both with age and usage, including wear, cracking, and fatigue, among others; whereas the residual life of a machine was characterized as remaining useful till its level of degradation arrives at a predefined failure threshold [12]. Real-time production data from complex systems produce a huge variety and volume of data. Handling this kind of data-intensive system with conventional statistical tools may be insufficient when firms seek to strategically conceal the data [13]. Hence, there is a need for advanced analytics such as descriptive, predictive, and prescriptive analytics to analyze the machine’s historical data to improve the efficiency of the system by knowing the health condition at every stage.

Given this scenario, towards summarizing the status of present research and to stimulate future investigations, the main aim of this paper is to carry out a Systematic Literature Review (SLR) with respect to the degradation and upgradation models for FUS. Hence, a review of manufacturing systems in the context of three analytics has been considered, particularly with flexibility as a key common word. The analysis of the reviewed literature enabled us to develop a comprehensive conceptualization as shown in (Figure 1). It is the conceptualization that was used to classify the findings and it was also referenced for future research.

Figure 1. Framework addressing the topics affecting flexible unit systems (FUS).

2. Discussion and Future Research Agenda

This paper presents the SLR using different articles to discuss degradation and upgradation models for flexible unit systems life. Some significant issues from the review are talked about in this section. Moreover, there is an opportunity to identify the number of research gaps, with suggestions for future work. The discussion follows the conceptualization that appeared in Figure 1. First, the 5 keywords that have been taken into consideration are (1) Degradation, (2) Residual life distribution, (3) Workload adjustment, (4) Upgradation, and (5) Predictive Maintenance. The keywords have helped us to find related journal articles by searching in the three databases in the selected research area. Authors such as [43,65,66] discussed different analytic techniques, for example, descriptive, predictive, and prescriptive, to analyze manufacturing data for achieving competitive benefits for the manufacturing industries.

Authors Hao et al. [12], Ben-Salem et al. [67], Peng et al. [68], Bian et al. [69], and Hajej et al. [26] worked on the degradation of different configurations, for example, series and parallel configuration manufacturing systems. Zhenggeng et al. [21] worked on degradation models and various stochastic processes like gamma process and Markov renewal process to find the degradation rate of manufacturing equipment. Zhang et al. [29] proposed conventional Wiener process-based degradation as one of the most important degradation model techniques among different degradation techniques. Naipeng et al. [70], Das et al [33], Si et al. [34], Zhang et al. [29], and Bian et al. [32] worked on finding the relationship between degradation rate and the residual life of a machine. The prediction of the manufacturing unit’s residual life will be helpful to reduce the degradation rate by adjusting the workload to maintain the maximum production rate.

Adam Robinson [48], Pavlov et al. [47], Garcia-Garza et al. [44], Grohn et al. [46], Du et al. [45], Menezes et al. [55], and Dong et al. [19] investigated upgradation of a manufacturing system, which will help to enhance the performance and reliability of manufacturing equipment. Spendla et al. [52], Dong et al. [19], Fang et al. [20], and Kaiser et al. [71] present the predictive maintenance of machines using sensors degradation data for calculating the time to failure of various machines. Traini et al. [49], Zhang et al. [50], and He et al. [53] worked on predictive maintenance analytics by considering recent past data to eliminate prospective failures and also to improve the mission dependability of production systems.

2.1. Research Opportunity 1: How Can Residual Life Be Predicted in FUS to Improve Systems Efficiency

Degradation is an unavoidable characteristic, which it requires the utmost attention to pursue. However, a lot of literature is already available to handle the degradation rate at the component level. A limited number of papers (Hao et al.[12]; Manupati et al.) [38] have considered system-level degradation, especially in the manufacturing systems context. A recent paradigm shift has forced the use of the Internet of Things (IoT) in almost every stage of the product life cycle. In addition, process industries have highly benefitted from the key technologies that emerged from this shift (Varela et al., [72] Varela and Ribeiro,) [73]. To make these processes effective and efficient, system-level health monitoring is new thinking among researchers paying attention to these issues. To improve the health status of the system, an individual system’s degradation rate needs to be decreased, which in turn improves the residual life of the machine. Here, the residual life of a machine was characterized as remaining useful time till its level of degradation arrives at a predefined failure threshold. The degradation and residual life follow different distributions depending on the order requirement and system status. Hence, this is a challenging work one can take into consideration to explore further.

2.2. Research Opportunity 2: How to Deal with Heterogeneous Data Obtained from Various Sensory Sources for Predicting the Degradation Rate of FUS?

Heterogeneous data includes multiple internal and external databases generated from different sources obtained in various dimensions (Varela and Silva, 2008 [74], Zhang and Gregorie, 2016) [75]. Real-time production data from complex systems produce a huge variety and volume of data. Handling these kinds of data-intensive systems with conventional statistical tools may be insufficient when firms seek to strategically conceal the data [13,76–78]; Hence, to handle the heterogeneous data in FUS and predict the degradation rate, improving the residual life advanced analytics is essential. This area opens wider challenges for the researchers to explore.

2.3. Research Opportunity 3: How to Develop FUS for Real-Life Problems?

In this section, we propose four different configurations derived from the real-life examples: i.e., one degree, two-degree, semi-flexible, and fully flexible, shown in Figure 2a–d. Where one-degree configuration is represented, it handles the requirements to process it in sequential order. The open braces (1, 1) represent the position and stage of the machine, e.g., (1, 4) in Figure 2a. Consequently, for two degrees of flexibility, the configuration is shown in Figure 2b, through which, after the jobs arrived and processed in the first machine are chosen for the next operation to process on the second machine, it has a flexibility of alternative machines available in the second position at the second stage. Hence, it has position flexibility, routing flexibility, and machine flexibility to execute the operations. Figure 2c,d represents the semi-flexible and fully flexible unit system, wherein in the semi-flexible configurations, the second operation can be processed on more than 2 machines unlike the restrictions presented in the previous systems. In the fully-flexible systems, the machines have the flexibility to process any operation at a time.

(a) One-degree flexible configuration	(b) Two-degree flexible configuration
(c) Semi-flexible configuration	(d) fully flexible configuration

Figure 2. A flexible unit system with different configurations.

This entry is adapted from the peer-reviewed paper 10.3390/fi13030057