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Full Article: Art Journals In Morocco: New Ways Of Seeing And Saying

By Ahmed Zainul Abideen 1 , Veera Pandiyan Kaliani Sundram 1, 2, * , Jaafar Pyeman 1, 2, * , Abdul Kadir Othman 1, 2 and Shahryar Sorooshian 3, 4

Background: As the Internet of Things (IoT) has become more prevalent in recent years, digital twins have attracted a lot of attention. A digital twin is a virtual representation that replicates a physical object or process over a period of time. These tools directly assist in reducing the manufacturing and supply chain lead time to produce a lean, flexible, and smart production and supply chain setting. Recently, reinforced machine learning has been introduced in production and logistics systems to build prescriptive decision support platforms to create a combination of lean, smart, and agile production setup. Therefore, there is a need to cumulatively arrange and systematize the past research done in this area to get a better understanding of the current trend and future research directions from the perspective of Industry 4.0. Methods: Strict keyword selection, search strategy, and exclusion criteria were applied in the Scopus database (2010 to 2021) to systematize the literature. Results: The findings are snowballed as a systematic review and later the final data set has been conducted to understand the intensity and relevance of research work done in different subsections related to the context of the research agenda proposed. Conclusion: A framework for data-driven digital twin generation and reinforced learning has been proposed at the end of the paper along with a research paradigm.

Digital twin technology creates relatively close connectivity between both the virtual and physical worlds, allowing you to monitor and command systems and components remotely. Moreover, it is now possible to run simulation models to test and forecast resource and process-related changes in various “what-if” scenarios. Hence, organizations are now getting significant benefits from digital twin technology that assists in mapping and analyzing details related to operations performance, product and service innovation, and shorter on time delivery [1, 2].

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The above-mentioned concept is one among the Industry 4.0 tools. Industry 4.0 is defined as the digitization of industrial processes, which incorporates the digitization of data as well as physical attributes [3]. Industry 4.0 is a tool that enhances the technological maturity level of any organizational system that allows for the adoption of digitalization, integration, and automation in the production and supply chain network [2, 3]. Furthermore, the fourth industrial revolution is centered on vertical and horizontal integration of the value chain. Businesses can only sustain their market position in an increasingly competitive market by leveraging the benefits of synergy between production management and logistics [4]. The benefits are realized through logistically integrated production management, including the design of the information system required for planning and execution [5]. Creating a near exact twin of a process or product with well-defined parameters and variables is digital twin. Simulation modelling is the tool to accomplish a digital twin [2]. There have been extensive studies where simulation modelling and the digital twin approach have been applied to study and analyze various operations of production and supply chain systems and measure how they impact organizational performance and development as a whole [6]. However, there are still minimal insights and implications on the results obtained when digital twin technology is assisted with various other I.R 4.0 tools [7]. Currently, various studies have focused on the ‘Know-How’ procedure that could help us build a descriptive model with historical data and conduct only a what-if analysis by changing the variable values for certain operational parameters in the simulation model [8]. Instead of historical data, efforts are being taken to capture and retrieve real-time data using the internet of things and big data technology to feed and simulate a prebuilt digital twin prototype mother file [9]. In this course of action, several disruptions, buffers, delays, and challenges are met in the real-time scenario where solutions for these challenges can also be assigned. Over a course of period these actions can help create a series of patterns (scenario vs. solution) which can be useful to build a prescriptive analytics platform. The authors of this study are keen on studying the development of research in the above-mentioned theory. Authors argue that this combination of digital twin and machine learning has largely been utilized in the medical field and but less researched from the perspective of production, supply chain, and logistics.

Therefore, this study aims to conduct a systematic review of the literature to systematize and study the research findings and implications in the area of digital twin and its benefits when it is coupled with reinforced machine learning to improve production logistics and the supply chain. This research will answer the following research questions: RQ1) What are the applications of digital twin simulation modelling in Supply chain and logistics? RQ2) What is the impact of digital twin and reinforced machine Learning on supply chain and logistics? RQ3) What are the prospects and scope for prescriptive modelling in supply chain and logistics? How will that ease the process of building a decision support system for a supply chain or logistics 4.0? This paper has discussed the research problem and purpose of study in the introduction part followed by a state-of-the-art literature review discussing past research work and gaps. Based on the study, the authors have proposed a conceptual framework in the discussion part and concluded research findings and implications.

The digital transformation promises newer opportunities. However, only fewer companies can set up Industry 4.0 based production systems [10]. The fourth industrial revolution had an impact not just on the manufacturing sector, but also on the supply chains that supported it. Industry 4.0 can only become a reality if logistics can provide the necessary input components to production systems at the proper time, quality, and location [11]. However, this can only be accomplished by using new technological solutions to efficiently design and manage a more coordinated material flow. Industry 4.0 technology adoption is becoming increasingly vital for businesses to optimize their manufacturing processes and organizational structures. Companies, on the other hand, sometimes struggle to create a strategy plan with newer business models. For a Logistics 4.0 transformation, the firm’s tendency toward logistics 4.0 is determined by the existing use of technologies in the logistics process, as well as the amount of investment towards innovation [12].

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Nonetheless, there are several obstacles to overcome when implementing Industry 4.0. For example, a lack of technological infrastructure makes implementation difficult. Furthermore, there is a scarcity of professionals and knowledgeable staff in this field who can establish a new system or renovate an existing one to achieve the best results [13, 14].

IR 4.0 has led to the digitization in supply chain and logistics has made way for the evolution of logistics 4.0. Various tools and technological settings from the IR 4.0 have been adopted in the supply chain and logistics setting or environment to leverage the benefits. Digital twin technology offers risk free scenario analysis by developing a predictive and prescriptive decision-making platform for the industry players and it is one of the core technological tools in IR 4.0 [11].

Basically, logistics is the sub-component of supply chain and supply chain is the sub-component of production management. A digitally equipped supply chain platform is the backbone for Industry 4.0 to function. I.R 4.0 tools equip the supply chain and logistics processes such as inbound logistics, warehouse management, intralogistics, outbound logistics and logistics routing, etc. I.R 4.0 based protocols and tools such as Smart data management, Internet of things, cloud computing and Blockchain accelerated the supply chain and logistics processes to greater extent. These create an automated, intelligent and increasingly autonomous flow of assets, goods, materials and information between the point of origin and the point of consumption, and the various points in-between are key. Supply chain logistics processes become more efficient, effective, connected, and agile/flexible in order to meet the needs of market [7, 10, 11, 12].