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The purpose of this paper is to present an original model for the production-recycling-reuse of plastic beverage bottles.

It is assumed that discarded two-liter plastic polyethylene terephthalate (PET) bottles are collected from the market. The bottles are then sorted into non-contaminated and contaminated streams. The non-contaminated PET bottles are either remanufactured or used as regrind mixed with virgin PET to produce new bottles to satisfy varying demand. The contaminated bottles are either sold to industries using low-grade plastic or disposed of in a landfill. Numerical studies are used to illustrate the behaviour of the model, with an emphasis on exploring the reduction of total system cost and the amount of bottles going into a landfill.

Numerical analyses conducted on the model found that the amount of bottles collected had the largest influence on the outcome of the total system unit time cost. Alternative materials to PET are surveyed and used to demonstrate a significant reduction in the cost of landfill disposal due to their more rapid degradation in the landfill.

Several areas for future work are highlighted. Potential modifications to the model could focus on accommodating bottles made of material other than plastic, incorporating the effects of learning on manual tasks, and on accommodating shortages or excess inventory.

The model incorporates several unique aspects, including accounting for the cost of land use and associated environmental damage through the calculation of a present value that is charged to the manufacturer.

The increasing demands for customized services and frequent market variations have posed challenges to managing and controlling the manufacturing processes. Despite the developments in literature in this area, less consideration has been devoted to the growth of business social networks, cloud computing, industrial Internet of things and intelligent production systems. This study recognizes the primary factors and their implications for intelligent production systems' success. In summary, the role of cloud computing, business social network and the industrial Internet of things on intelligent production systems success has been tested.

Intelligent production systems are manufacturing systems capable of integrating the abilities of humans, machines and processes to lead the desired manufacturing goals. Therefore, identifying the factors affecting the success of the implementation of these systems is necessary and vital. On the other hand, cloud computing and the industrial Internet of things have been highly investigated and employed in several domains lately. Therefore, the impact of these two factors on the success of implementing intelligent production systems is examined. The study is descriptive, original and survey-based, depending on the nature of the application, its target and the data collection method. Also, the introduced model and the information collected were analyzed using SMART PLS. Validity has been investigated through AVE and divergent validity. The reliability of the study has been checked out through Cronbach alpha and composite reliability obtained at the standard level for the variables. In addition, the hypotheses were measured by the path coefficients and R², T-Value and GOF.

The study identified three variables and 19 sub-indicators from the literature associated that impact improved smart production systems. The results showed that the proposed model could describe 69.5% of the intelligence production systems' success variance. The results indicated that business social networks, cloud computing and the industrial Internet of things affect intelligent production systems. They can provide a novel procedure for intelligent comprehensions and connections, on-demand utilization and effective resource sharing.

Study limitations are as below. First, this study ignores the interrelationships among the success of cloud computing, business social networks, Internet of things and smart production systems. Future studies can consider it. Second, we only focused on three variables. Future investigations may focus on other variables subjected to the contexts. Ultimately, there are fewer experimental investigations on the impact of underlying business social networks, cloud computing and the Internet of things on intelligent production systems' success.

The research and analysis outcomes are considered from various perspectives on the capacity of the new elements of Industry 4.0 for the manufacturing sector. It proposes a model for the integration of these elements. Also, original and appropriate guidelines are given for intelligent production systems investigators and professionals' designers in industry domains.