1 Introduction

The concept of “Industry 4.0” has gained the curiosity of both academics and corporate executives, which is already a well-known phrase in academic institutions. The topic has been studied in universities for quite some time, but the phrase “Industry 4.0” has only lately come up and is gaining a lot of traction, both in academic circles and in the field of industrial economics [1]. In light of the current cutthroat business climate, many corporations are putting a premium on incorporating intelligent technology into their manufacturing systems. Improved operational efficiency, reduced risk, more environmental sustainability, and higher product quality are the goals of this strategy shift. Because of the positive effects it could have on industrial processes as well as environmental sustainability, the idea of Industry 4.0 has therefore gained a lot of traction in the business world. Manufacturing companies’ operational and policy frameworks have been greatly affected by the introduction of Industry 4.0. Manufacturing enterprises can leverage automation solutions facilitated by the swift advancement of IT and IoT inside the manufacturing domain [2]. The potential benefits of this technological advancement for manufacturers in terms of enhancing production efficiency through the utilization of intelligent technology, specifically the Internet of Things (IoT) and information technology (IT), are substantial. However, the fertilizer industries in Bangladesh face significant limitations that hinder their ability to integrate these technologies into their manufacturing systems. Consequently, it is imperative to evaluate the present situation and examine the obstacles faced by industrial enterprises in their endeavor to integrate Industry 4.0. Numerous researchers have conducted various investigations pertaining to Industry 4.0 initiatives; however, thus far, none of these studies have recognized or investigated the problems companies face when attempting to implement Industry 4.0 in the fertilizer business. This study’s necessity has been effectively justified and supported.

1.1 Significance of this study

Sustainable production opportunities on Industry 4.0 were recognised by Stock and Seliger [3], and the impact of smart manufacturing systems on Industry 4.0 was examined by Waibel [4]. For small and medium-sized regional enterprises, Faller and Feldmúller [5] examined the learning aspect of Industry 4.0. Lu and his team conducted a comprehensive analysis of overall Industry 4.0 technologies and their applications [1]. This current study primarily concentrates on the commercial fertilizer producing plants in Bangladesh, as commercial fertilizer is predominantly utilized in the agricultural sector. Although organic fertilizers are available as an option, their quantity is insufficient to meet the demands of the large-scale agriculture industry. First of all, no relevant studies have found in literature regarding implementing Industry 4.0 in the commercial fertilizer producing sector in any country. Some studies have been found on literature regarding implementing Industry 4.0 in other than fertilizer sectors. As an example, Moktadir an his team conducted research on how the leather industry in Bangladesh could benefit from Industry 4.0 and their possible challenges [6]. They have ranked total ten challenges they identified in their investigation. Next, Abdus Shabur investigated the difficulties in integrating Industry 4.0 in Bangladesh’s apparel industry [7]. Based on their research, they have determined six main obstacles. In their study on Bangladesh’s fourth industrial revolution, Rumi identified five major problems [8]. In their study regarding the implementation of Industry 4.0 in Bangladesh, MA Islam have identified six major hurdles [9]. Some recent studies on automation in fertilizer mixing and automated agricultural process have been found in literature. For example, AH Ishak worked on autonomous fertilizer mixer through the Internet of Things (IoT) [10]. Cyril Joseph performed research on automated fertilization system for efficient utilization of fertilizer and water [11]. Meanwhile, Bramley and his team conducted a study on the views of farmers toward the utilization of sensors and automation in making decisions about fertilizer usage, especially Nitrogen fertilization in the Australian grains sector [12]. Giri conducted research on the use of Internet of Things (IoT) to enable agricultural automation and optimize the usage of water, fertilizer, and pesticides [13]. Shabur conducted research on the barrier and opportunities in implementing Industry 4.0 in steel sector of Bangladesh [14]. But no relevant studies have been found on literature about current status and barriers of implementing Industry 4.0 in fertilizer sector. Only a South African fertilizer manufacturing company Bagtech emphasizes on the importance and benefits of Industry 4.0 in the fertilizer sector on their website [15]. These above studies show how little attention has been paid to figuring out how to determine where we are in the Industry 4.0 process and what obstacles stand in the way of a full rollout, especially in fertilizer sector.

Furthermore, it is worth noting that Bangladesh is predominantly reliant on agriculture as its primary economic sector. There is a significant need for fertilizer to meet the national demand. Bangladesh possesses a substantial number of fertilizer factories that are held by both public and commercial entities [16]. The majority of these factories were created more than twenty years ago. This plant has been enhanced with state-of-the-art technologies and the latest innovations of the current era. Consequently, their inability to meet the annual fertilizer production quota arises. Consequently, Bangladesh annually imports a substantial quantity of fertilizer from China, Vietnam, and other nations. In December alone, fertilizer imports totaling 18,968 million Taka were made to fulfill the national demand [17]. Bangladesh possesses an adequate number of manufacturers; nevertheless, these establishments have not been modernized with the latest technologies. It is critical to apply Industry 4.0 in order to decrease energy consumption in this industry, considering Bangladesh’s power and energy conditions [18]. Hence, it is imperative to conduct a research that specifically examines the integration of new technologies and tools (Industry 4.0) in this industry, with the aim of improving the efficiency and overall quality of the fertilizer produced. This study aims to fill the gap in research by presenting a methodology for identifying the current state of the fertilizer industry in Bangladesh and analyzing the challenges associated with implementing Industry 4.0. And ideally, this study will be the first attempt globally, as well as in Bangladesh, to implement Industry 4.0 to the fertilizer industry.

Agriculture has a crucial role in the economy of Bangladesh as stated earlier. It enhances the country’s Gross Domestic Product (GDP), generates employment opportunities, and plays a crucial role in ensuring food security. Bangladesh is predominantly an agricultural society, with the majority of its population engaged in agriculture either directly or indirectly. Based on the fiscal year of 2021–2022, agriculture makes up approximately 11.5% of the GDP [19]. For the crop cultivation methods used in modern agriculture, fertilizers are essential. Fertilizers are one of the components that significantly impact crop production and help to boost output. In fact, as they account for nearly 50% of overall production, inorganic fertilizers are crucial to the efficacy of Bangladesh’s agricultural production systems [20]. As a result, Bangladesh’s fertilizer industry is booming, and the country could benefit from smart production and other sustainable production methods to make the best decisions for the environment and boost productivity to achieve its objectives. Thus, this research is anticipated to provide an opportunity for fertilizer manufacturers in Bangladesh to use Industry 4.0 technology in their plants and grasp its advantages.

A state-of-the-art multi-criteria decision making (MCDM) method known as the “Best–Worst Method” is employed to examine and assess the barriers to implementing Industry 4.0 after an industry survey discloses the current state of application. Therefore, the following are the intended outcomes of this study that add to the already existing reservoir of knowledge:

  1. (i)

    To analyze the existing reality of industry 4.0 that has already been deployed.

  2. (ii)

    To determine the barriers/challenges of implementing Industry 4.0 in the fertilizer sector through an industry survey.

  3. (iii)

    To analyze and prioritize such challenges using Best–Worst Method and to perform sensitivity analysis.

  4. (iv)

    To figure out various potential solutions to those challenges.

To assist in achieving these goals, the associated literature is being examined in order to identify some possible critical difficulties for adopting Industry 4.0 in the fertilizer manufacturing sector. The authors met with a group of industry managers and professionals from Bangladesh’s well-known fertilizer companies to identify the most pressing issues.

In this investigation, the Best–Worst Method (BWM) was used. When faced with solving a problem that involves numerous decision-making criteria, this method outperforms others, such as the analytical hierarchy approach (AHP) and the Decision Making Trial and Evaluation Laboratory (DEMATEL) technique. Specific and observable benefits distinguish this method and its associated resources from alternatives. Researchers and decision-makers can save time and effort with this method’s improved accuracy and reliability compared to earlier MCDM techniques, all while using fewer pair-wise comparison matrices to get the same or better findings [21].

The following is how the paper’s reminder is organized. Section 2 delves into the philosophical foundations of Industry 4.0, the technological innovations employed in Industry 4.0, and the obstacles associated with implementing Industry 4.0. Section 3 discusses study methodology, including research design and the best–worst strategy. Section 4 outlines a discussion of the results and sensitivity analysis. Section 5 displays the possible solutions of selected challenges. Finally, Sect. 6 discusses the study’s conclusions, industrial and practical consequences, limitations, and ideas for future research.

2 Theoretical background

2.1 Industry 4.0

For several industries, the concept of Industry 4.0 is now being widely discussed. This issue also influences the fertilizer industries. The German High Tech 2020 Strategy first used the term “Industry 4.0” in 2011. The term “Industry 4.0” refers to the impending the fourth industrial revolution, which will see increased efficiency and productivity in business operations brought about by automated manufacturing initiatives. [22]. Europe, especially the German industrial sector, uses the term “Industry 4.0” frequently [23]. A combination of IoT, artificial intelligence (AI), cyber-physical systems (CPS), big data analysis and industrial automation forms the basis of the “Industry 4.0” concept. Four facets can elucidate the essential characteristics of Industry 4.0: (1) Smart manufacturing systems and complete vertical integration across the entire value chain; (2) Present tendencies and horizontal collaboration across the complete value chain; and (3) innovative technologies and acceleration. And (4) throughout the engineering process across a product’s lifetime [24]. With the integration of a cyber-physical manufacturing process, a manufacturing facility can undergo a digital transformation and gain intelligent capabilities. Because of this, innovative production processes can flourish, and vertical integration can be achieved all the way along the value chain. With a keen understanding of market dynamics, this approach can lead to a production system that is robust and adaptable, capable of swiftly responding to fluctuations in inventory and market conditions. Throughout the engineering process that takes place throughout a product’s lifespan. The goods and resources in this system are connected through vertical integration, creating a seamless flow between different stages of production [25]. Smart sensor technology is being utilized for network monitoring in Industry 4.0, a vision of cyber-physical and corporate networks that enhance process automation, adaptability, and efficiency [26]. Optimal networks with integrated transparency and great degrees of flexibility are generated by this technology. A rapidly changing manufacturing system that encompasses all aspects of the process network, from procurement to delivery, is created through horizontal integration [27]. Intelligent acceleration with the help of technology is the third essential quality. The impact of smart technology on manufacturing procedures is substantial [28]. Smart technology can help to speed up the whole manufacturing process by reducing production time and lowering costs. Industry 4.0 necessitates the integration of automation into the manufacturing process. An increasingly autonomous production process is within reach with the help of smart technologies like advanced robots, AI, and monitoring devices [29]. One of the key attributes of Industry 4.0 is its comprehensive integration throughout the whole product life cycle, encompassing various stages, such as raw material procurement and final disposal. This integration involves the intelligent incorporation of advanced technologies and digitization processes [23]. More adaptable manufacturing processes may be possible if data is accessible at all stages of a product’s life cycle.

Based on the discussion that follows, it is evident that Industry 4.0 introduces a transformative shift in traditional production and management processes through the implementation of novel technological concepts. In the upcoming section, we will provide a concise overview of the principal technologies and innovations associated with Industry 4.0.

2.2 Technologies associated with industry 4.0

Fertilizer manufacturing process requires a lot of machineries like rotary drum, mixing chamber, temperature and humidity controller, material handling equipment, weight and packaging machine, quality control equipment and so on [30]. So there are lot of opportunities to implement automation and smart production system in fertilizer manufacturing industries. Another important aspect of Industry 4.0 in fertilizer plants is the management of human resource and business operations. The quality of fertilizer greatly depends on ingredients and chemical reaction. If the raw material collection process is enhanced by new technologies of Industry 4.0 and the chemical process is accomplished by Cyber-Physical systems, surely the overall quality of the fertilizer will be increased.

4th Industrial revolution highlights the global technology transformation. robotics, cloud computing, cyber-security, simulation, the industrial internet of things, additive manufacturing, big data analytics (BDA), machine learning and augmented reality are needed to integrate to Industry 4.0 as shown in the Fig. 1. [31]. In order to be ready to accept Industry 4.0, several organizations are adopting such technologies into their production systems. With the use of BDA technologies, real data may be analyzed to boost efficiency and remove uncertainty from decision-making [25]. The capacity of this instrument to simplify and environmentally friendly their supply chains is advantageous to several industries, including industrial manufacturing, pharmaceuticals, chemicals, and autos.

Fig. 1
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Key technologies of Industry 4.0 [32]

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One of the most important tools for Industry 4.0 is the autonomous robot in the fertilizer industries. Material mixing and handling can be greatly benefited by the robotic application. Businesses can benefit from the assistance of an autonomous robot in situations where human workers are unable to or are restricted from performing tasks with greater accuracy [25]. Every year, new types of more adaptable and efficient robots are introduced, accounting for around 14% of all active industrial robots. In order to keep up with customer demand, manufacturers of the future will most likely use a combination of human and robotic workers [22].

A real-world fertilizer production system is replicated using machines, people, and items. Scientific modelling allows users to see how chemical and operational systems work, safety engineering helps keep systems secure, and technology simulation helps optimize designs [33]. These days, many factors, such as process cycle time, design ease and comfort, and efficiency, are evaluated in the industrial sector through the use of two- and three-dimensional simulations [34]. With the use of simulation, Industry 4.0 might reduce production failures, waste, and unnecessary resources.

Fertilizer production, with the help of Industry 4.0, is greatly aided by the Internet of Things. The fact that IIoT makes use of every facet of the internet to facilitate production has earned it the moniker “Industrial Internet of Things” [22]. In order to enhance industrial processes, the Internet of Things incorporates virtual data for operational objectives. IoT software is able to plan and operate equipment with intelligence [35].

One of the biggest obstacles to Industry 4.0’s broad adoption is cyber security. To run efficiently, a fertilizer manufacturing business requires connectivity and the implementation of a common statement code of behavior. Because every fertilizer manufacturer employs some confidential procedures which should not be disclosed to anyone. That’s why more safe, advanced, and trustworthy frameworks for machines and operators are required to defend the smart production system from the risk of cyber security [25]. A crucial aspect in the progression towards “industry 4.0” is establishing a connection between the physical environment and the digital data necessary for activities such as manufacturing scheduling, preparation, and performance assurance [36]. Integrating digital and physical infrastructures is made easier with cyber-physical systems. Data mining aids industrial route prediction and smart automobiles based on cyber-physical systems for warehouse management [25].

Fertilizer production relies heavily on the storage and use of data in real time. The massive amounts of data collected in real-time for use in production are stored on the cloud as part of the fourth industrial revolution, often known as Industry 4.0 [37]. In a factory setting, it could be helpful for companies to connect and share communication equipment. Cloud computing enables digital manufacturing by connecting organizations globally [22].

Technologies based on additive manufacturing are used to speed up and lower the cost of the fertilizer production system. Industry 4.0 advocates for the harmonization of manufacturing systems with intelligent technology. The technique of additive manufacturing represents one of them in order to fulfil some of the most crucial needs associated with the Industry 4.0 [38]. This strategy allows manufacturers to maximize output for a limited number of distinctive items. Reduced shipment times and inventory requirements are two additional benefits of manufacturing additives [22]. Because it allows for quick design iterations, additive manufacturing helps fulfil changing market expectations. To keep up with the ever-increasing demand from their customers, some firms nowadays adopt additive manufacturing technology.

Augmented reality, a system of communication that provides technical help to many sectors through various channels of communication, is utilized by the fertilizer production facility. With the use of augmented reality, companies can get instantaneous consumer input, which they can utilize to improve working conditions and test out different ideas [22]. Because it both explains processes and gives visual cues while workers carry them out, augmented reality is ideal for repair and maintenance jobs.

Big data refers to the massive volumes of data, both organized and unstructured, that a fertilizer plant may potentially receive at any one moment. Machine learning is an evolving computational approach that enables the extraction of significant insights and effective decision-making from large datasets [39]. Machine learning keeps an eye out for problems in production, finds errors, and estimates future needs [40]. Machine learning techniques have been presented for varied purposes by several writers. The use of vibration sensors and acoustic emissions in the diagnosis of gearbox defects was contrasted by Qu and his team [39], while To help with defect severity categorization and finding its source [41]. A multilayer neural network architecture was developed by Zhou [23]. Thus, machine learning is crucial for analyzing defects and improving processes in manufacturing enterprises.

The aforementioned technologies are very crucial for a modern fertilizer manufacturer to meet the increasing demand with best quality. If these technologies can be implemented completely in a plant, it is expected to take the best advantages from Industry 4.0 in the modern manufacturing era.

3 Methods

3.1 Research design

A multi-criteria assessment study is necessary to evaluate the challenges of adopting Industry 4.0. The goals of this research were to better understand the current situation and the barriers to implementing Sector 4.0 in Bangladesh’s fertilizer industry through an industrial survey. Survey research is a distinctive method of collecting data from a large population. Surveys offer several benefits, such as the capacity to collect substantial quantities of data, the availability of validated models, and a large population, which results in increased statistical power. Survey research methods can gather quantitative and qualitative data to fully understand our target population. Industry analysis is to identify organizations’ competitive possibilities and dangers. We can easily understand how industry forces affect survival. The problems associated with implementing Industry 4.0 have been identified by a comprehensive examination of relevant literature, feedback from industry management, and analysis by academic experts. In-person attendance at the survey was only allowed for directors and managers in charge of supply chain networks, operations, and information technology. Prior to collecting data from them, they were thoroughly educated on every aspect of Industry 4.0 and its implementation in their facilities, including anticipated obstacles. In a nutshell, the survey attendees were made knowledgeable and capable of providing data about Industry 4.0. Similarly, the author collected data from academic specialists who are highly recognized in the field of Industry 4.0 and are presently engaged in research on the implementation of Industry 4.0. This selection assures the reliability and acceptance of the study. The new BWM approach was used to assess the problems that were discovered. What follows is a synopsis of the unique BWM-based MCDM approach that has been proposed. And finally some feasible solutions were suggested from authors to implement the overall Industry 4.0. Figure 2 depicts the steps of the overall study.

Fig. 2
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Flow chart of this study

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3.2 Best worst method

Resources for MCDM are widely available. One of the most helpful resources is the “Best Worst” MCDM tool that Professor Rezaei designed in 2015 [42]. If attempting to handle a problem with various decision-making criteria, this strategy is better than others, such as the analytical hierarchy approach (AHP). This has some clear advantages over other approaches and resources. The method helps researchers and decision-makers get better results faster by using fewer pairwise comparison matrices to achieve better results and by delivering more dependable outcomes than prior MCDM methodologies [43]. This research relies heavily on all of the aforementioned benefits from this MCDM platform.

There is another popular decision-making approach is DEMATEL. The DEMATEL methodology may have the following potential drawbacks when compared to BWM methods: The ranking of alternatives is determined based on their interdependent relationships, without considering any other factors in the decision-making process [21]. However, in the case of BWM, this issue may be circumvented. All criteria are taken into account while making decisions on any individual criterion. This is the reason why BMW has been selected for the present investigation.

There is solid groundwork in the BWM literature. No small number of scholars across many disciplines have made use of this practice. As an example, the medical tourism development strategy was studied by Abouhashem Abadi [44] using BWM. Badri Ahmadi [45] used BWM to evaluate supply chains’ social sustainability, and Guo and Zhao [37] employed fuzzy-based BWM to address the MCDM problem. Using a combination of conventional and eco-friendly standards, Rezaei [46] utilized the BWM to select their providers. To develop a framework for risk assessment, Torabi [47] used BWM. Among BWM’s many applications are the following: the evaluation of eco-industrial parks’ benefits [48], the selection of biomass technology [49], and research into the ways in which external variables impact gas and oil distribution networks for environmentally friendly growth [50].

4 Results and discussion

4.1 Industry visit and survey

Preliminary data on the current status of Industry 4.0 installations has been collected by conducting site visits to five important fertilizer firms in Bangladesh. We were granted access to all five fertilizer plants, where we were able to visit their factories and obtain comprehensive data from well-informed employees. The current status of sector 4.0 in the fertilizer sector of Bangladesh was the primary impetus for this research. We gathered data from the aforementioned industries covering every aspect of Industry 4.0 in order to accomplish this. To obtain this information, authors ensured many essential factors universally. Prior to collecting data, the participants of the survey were educated on each component through appropriate training. Upon arrival at the factory, all participants were given a concise overview of Industry 4.0. Next, an elaborated overview of the process of marking and its significance was provided through a seminar. 0% value indicates the absence of a certain element in the plant, whereas a 100% value indicates that the installation of that piece has been completed in its entirety. So it can be said that during the marking process, the attendees were well-informed about the key attributes of each element, taking into consideration. Ultimately, the submitted data underwent scrutiny by a higher authority (plant managers and directors) within the facility to guarantee the data’s dependability. The data pertaining to each element was gathered by considering certain distinguishing characteristics of such element, as presented in Table 1.

Table 1 The five industries’ average existing installation of Industry 4.0 elements
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The data presented in Table 1 provides an overview of the current state of Industry 4.0 implementation in five fertilizer plants. Only 5 out of the 11 essential elements have achieved an average implementation rate of above 50% across the five plans. It is important to highlight that this survey was conducted just to gather information about the present state of Industry 4.0. Since these statistics are manually submitted, there is a possibility of some inaccuracies.

However, the data was properly validated by the author who visited all plants and also by higher management authorities, as previously noted. Therefore, these data can be considered credible. Nevertheless, despite encountering an unforeseen inaccuracy, this table undeniably presents a comprehensive overview of the Industry 4.0 landscape in the fertilizer manufacturing industry, which is the main focus of this survey. The primary objective of this study is to identify and assess the obstacles and their respective significance in the implementation of Industry 4.0. The result of BWM was further assessed by performing sensitivity analysis to strengthen the quality of ranking.

4.2 Assessing the difficulties related to the adoption of Industry 4.0

None of these plants have installed more than 50% of the anticipated Industry 4.0 components, according to the supplied data. Using a questionnaire, the writers have investigated the causes of this occurrence. The 12 issues given to the industrial specialists are displayed in Table 2. The majority of the research on these twelve obstacles came from more recent articles about Industry 4.0 as shown in Table 2, total combined twelve challenges were chosen firstly. Table 2 shows those 12 challenges with their short description and references.

Table 2 Nine challenges that were ultimately chosen
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But it was not sure whether those twelve challenges are applicable to the fertilizer sector or not. Therefore, the industry respondents were requested to provide their feedback on refining the problems by expressing their perspective on each difficulty as either ‘Yes’ (positive), ‘Somehow YES’, or ‘No’ (negative). A fixed onset rule has been fixed in such way that if any challenge receives two or more ‘NO’, it will be discarded. From Table 3, it is clear that challenge 10, 11 and 12 i.e. unbalanced communication between the various manufacturers, environmental consequences, and lack of willingness of corporate owners got ‘NO’ two or more times, because of this, these three issues were ruled out for additional consideration, and nine issues in all were found to be major barriers to the Industry 4.0 adoption process.

Table 3 Potential obstacles to Industry 4.0 implementation
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4.3 Ranking the aforementioned challenges using the best–worst method

We now have nine obstacles in mind. One of the most important and integral parts of this research was ranking these challenges using the Best–Worst method (BWM) to understand their comparative significance. To meet that need, we gathered input data for BWM from experts in both industry and academia to ensure the quality of this research. First, we gathered information from five fertilizer mill experts. Following that, data from academicians was gathered. Those academic specialists were chosen from Bangladesh’s leading universities and are currently concerned about the Industry 4.0 and its implementation. All the specialists are assistant professors or higher. Table 4 contains more information on them.

Table 4 Experts’ details from Bangladesh’s leading universities
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In the subsequent section, the utilization of the best worst approach is demonstrated.

4.4 Choosing the Best as well as the worst challenge

All ten specialists, five from the industrial world and five from the academic world, came together in this stage to determine which difficulties were the most difficult and which were the least difficult as shown in Table 5.

Table 5 Ten experts identify the best and worst challenges
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4.5 The selection of the best criterion among several criteria by the utilization of a 1–9 point scale

This is the part when the experts check the best-to-others comparison matrix. Generally speaking, a higher score for one criterion indicates that it is more significant than the others. In relation to the rating system, the greatest criteria is equivalent to another criteria. Table 6 clearly shows that the superior criterion is much preferred over the alternative criterion, based on the scale of rating 9.

Table 6 Selecting best to others vector
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4.6 Utilizing a 1–9 point rating scale, calculating which of the other criteria are more important than the worst one

During this phase, decision-makers evaluate each option down to the lowest possible outcome vector. Which criterion is favored over the others is indicated by the rating scale. Based on the rating scale 1, the best condition is equally favorable to the other criterion. Based on the rating scale 9, the best criterion is substantially preferred over the other criterion. Table 7 displays the other vectors ranked lowest by all experts (Table 8).

Table 7 Choosing others-to-worst vector
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Table 8 The final ranking of the challenges showing their respective weights
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4.7 Calculating the optimum weights

By resolving the linear programming problem with the Best Worst Method solver, which was described earlier, it is possible to get the weights and ξL that are the most favorable. The stability of the comparison matrix is represented by the symbol ξL. The value of ξL that was estimated for ten different calculations is presented in Table 9. A value of 0.072896, which is close to zero, is the average value of ξL values. Consequently, this demonstrates that the system is extremely consistent, which makes it possible to make more accurate comparisons between various systems. The proper weights of ten experts are listed in Table 8.

Table 9 The ξL value for 10 distinct assessments of data
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One of the main goals of this study was to rank the obstacles associated with implementing Industry 4.0 in Bangladesh’s fertilizer industry. The final ranking can be derived from the Table 8. To compile this list, we gathered data from a total of nine professionals from the business and academic worlds. After a thorough analysis, challenge-6 (Insufficient understanding about Industry 4.0) was determined as the most significant challenge with the largest weight of 0.1652 as shown in Fig. 3 as well. It means that most of the company owners and staffs have poor knowledge about fourth industrial revolution which costs more for them. The second challenge i.e. “Large-scale investment in infrastructure” has been identified as the second important challenge with a weight of 0.1536. It implies that integrating all the components of Industry 4.0 will cost a lot of money, which the fertilizer companies will not accept. That is why business owners regard it as a major impediment. Then challenge-1 (Inadequate IT system) comes with the third place (weight 0.1341) which indicate that our fertilizer industries lack of a strong information and communication facilities that is very much needed to implement the fourth industrial revolution. After that challenge-5 (Lack of a comprehensive plan for Industry 4.0) appears as the 4th important barrier with a weight of 0.1233. So it’s evident that all fertilizer company owner/stakeholders lack of adequate policy to adopt this new industrial revolution.

Fig. 3
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Pie chart of weight of challenges

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Then comparable weights of 0.1140 and 0.1038 (challenge-9: Problems of rearranging the production pattern & challenge-8: Insufficient management team expertise) comes with fifth and sixth position, respectively. A crucial fact is the reconfiguration of the production pattern. All the fertilizer plants have a systematic production pattern. As a result, it is extremely difficult for them to rearrange those patterns and install new structures. A lack of skilled management personnel is also a significant impediment. It is critical to have available skilled and hardworking individuals who are intelligent about Industry 4.0.

Again challenge-7 (Insufficient governmental support) and challenge-3 (Data security concern) have a close weight of 0.775 and 0.727 with 7th and 8th position, respectively. This indicates that obtaining government support to execute Industry 4.0 is now nothing to worry about. Also eager to implement Industry 4.0 in as many sectors as possible is the government of Bangladesh. Experts believe that this barrier is less significant as a result. Additionally, because cyber-physical systems (CPS) generate a lot of data and every business has some sensitive information, it is essential to protect that information. Although it should go without saying, modern security systems allow for the safe storage and retrieval of data while providing the necessary security measures. Therefore, experts consider these difficulties to be incidental. Finally, challenge-4 (Possibility of losing job) stands as the least important barrier with a weight of 0.558. It indicates that job loss due to implementing Industry 4.0 is not a concern anymore. It was assumed in some years before that many people may lose their job due to automation and new technology. But in the real case, it is observed that the staffs and employees who are aware of learning new technologies and innovation never lose their job. Additionally, they are being hired to a better position because of their skills and experiences. That’s why experts think this challenge will not bother anymore to adopt Industry 4.0 in fertilizer industries.

4.8 Sensitivity analysis

It is imperative to assess the presence of bias in the obtained results while utilizing the MCDM approach. Consequently, certain researchers suggested conducting sensitivity analysis for the MCDM approach by varying the values of the highest ranking criterion within the range of 0.1–0.9, in order to observe the impact on the remaining criteria. The outcomes of this sensitivity study provide decision-makers with the assurance that the findings are either more dependable or not. The study examined the impact of “Insufficient knowledge about Industry 4.0 (CL-6)” on other identified challenges in adopting Industry 4.0 by assigning weights ranging from 0.1 to 0.9.

Table 10 summarizes the relative significance of each problem for executing Industry 4.0 when the weight of challenge-6 is changed from 0.1 to 0.9. Consequently, Table 11 presents the ranking of the challenges.

Table 10 Sensitivity analysis of the challenges’ weights
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Table 11 The chosen challenges’ ranking using the sensitivity analysis
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Simultaneously, the weights of other challenges are modified. Based on a sensitivity study, Table 11 ranks the challenges involved in adopting Industry 4.0. Tables 10 and 11 show that during sensitivity analysis, the problem “Insufficient understanding about Industry 4.0 (CL-6)” is ranked first most of the time, whereas the challenge “Possibility of Job Loss (CL-4)” is ranked last the majority of the time. Figures 4 and 5 illustrate the variations in weight as well as rankings that occurred during the sensitivity study. Hence, sensitivity analysis supports the conclusion that results obtained using BWM are uniform, unbiased, and more trustworthy.

Fig. 4
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Industry 4.0 challenges’ weights during sensitivity analysis

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Fig. 5
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Sensitivity analysis-based rank of the selected challenges

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5 Proposed solutions of challenges

The primary objectives of the paper were to(1) identify potential problems with implementing Industry 4.0 and(2) propose workable solutions to such problems. The previous phases involved identifying the problems and evaluating them according to their relative importance, using data supplied by nine academic and business professionals. In the next part, we will provide some practical solutions to these problems based on the author’s own judgment, expert’s suggestions and from established literatures in some instances.

5.1 Solution to the CL-1 (inadequate IT system)

One of the most important factors in the widespread adoption of Industry 4.0 is the growth of IT. Improving the IT infrastructure is possible via the use of a number of methods:

  1. i.

    Integrating cloud computing into a company’s operations

  2. ii.

    Improving the site’s security

  3. iii.

    Considering the age of the infrastructure.

  4. iv.

    Increasing the server’s capabilities

  5. v.

    Making more storage space

  6. vi.

    Creating system backups

  7. vii.

    Training the entire team about better IT activities

5.2 Solution to the CL-2 (large-scale investment in infrastructure)

Efforts related to Industry 4.0 are being heavily funded. It is crucial for a transformative business to invest in technology with caution. If businesses wish to keep their competitive advantage, they need to prioritize innovation and strategic investments. Industry 4.0 technology, like other major investments, need quantitative and qualitative examination. It takes into account the benefits, expenses, and total profitability of the firm, both in the short and long term [66]. A thorough understanding of the company’s strategic position and capabilities is necessary for conducting an investment evaluation. Having clear strategic goals in mind facilitates more productive conversations and choices regarding their attainment. Recognizing that the benefits and drawbacks of using I4.0 concepts vary between enterprises. This can be resolved by the government through the use of low-interest loans. To hasten adoption, we need robust monetary and political incentives, the correct kind of investment in education and training, and better technologies that make implementation easier. This issue has a solution: JICA, ADP, and other forms of foreign investment [14].

5.3 Solution to the CL-3 (data security concern)

Manufacturing is the second most attacked industry when it comes to cybercrime, even though it has a lesser degree of protection. Intelligent manufacturing facilities are much like any other network: they may be breached in a variety of ways, including by exploiting security holes, installing malicious software, launching denial-of-service (DoS) attacks, hacking devices, and more. When it comes to cyber-attack detection and defense, manufacturers confront new hurdles with the intelligent factory’s enlarged attack surface. With the expansion of the Internet of Things (IoT), new threats have emerged, presenting serious physical concerns, especially in the issue of the IIoT [67]. Protecting intellectual property from attacks must be a top priority due to the growing number of networked systems. Aligning defensive measures with the criticality of corporate activities is the notion of risk-based industrial security. Accurate real-time inventory of all OT assets is of the utmost importance.

5.4 Solution to the CL-4 (possibility of losing job)

There are a number of advantages to using smarter equipment and gadgets, and new technologies make it easier and cheaper to create and use innovative things. But some fear that automation will lead to employment losses. A recent analysis by McKinsey estimates that automation in manufacturing has led to the loss of jobs for 5.7 million people, most of whom lack the necessary skills [68]. A large portion of the economy, including control and automation systems, will be impacted by the fourth industrial revolution. The incorporation of AI, smart systems, robots, and automation into many industries holds immense promise, but being well-prepared is essential for navigating changes in the job market. New technology should be taught to every employee. Companies should invest in employee training to make the transition to new systems and technologies smooth and to allay workers’ fears of the unknown. Never stop learning if you wish to maintain your relevance and be able to adjust to new changes [69].

5.5 Solution to the CL-5 (lack of a comprehensive plan for Industry 4.0)

This study’s findings provide seven key recommendations for companies looking to implement an Industry 4.0 strategy and thrive in the modern digital marketplace [70].

  1. I.

    It would be beneficial for us to establish clear boundaries for our company and ensure that our goals align with the overall vision. Optimizing the value chain, boosting operational efficiency, and establishing the feasibility of building new business models should guide our goal-setting and priority-setting.2) Foster an environment that welcomes new ideas, is flexible, and isn’t afraid to try new things.

  2. II.

    The company needs to figure out what skills employees need, whether they can get them internally or via outside vendors.

  3. III.

    Organizations in this field need to assemble and manage high-performing teams, with an emphasis on diversity and the ability to use data to their advantages.

  4. IV.

    Selecting a set of providers who have tried and tested technology is the first step in constructing the most effective network of partners available.

  5. V.

    The organization need to grow an ecosystem attitude and become proficient in network administration.

  6. VI.

    The business should put their ideas to the test with a select group of individuals, verify their findings, and then arrange their learning strategies [71].

5.6 Solution to the CL-6 (insufficient knowledge about Industry 4.0)

Robots, the internet of things (IoT), and smart machinery are just a few examples of the disruptive technologies that will supplant many low-skill, physically demanding jobs. Managers and employees in the industrial sector must immediately begin to expand their theoretical understanding and practical skill sets. A recent analysis by McKinsey estimates that automation in manufacturing has led to the loss of jobs for 5.7 million people, most of whom lack the necessary skills [72]. Not only that, but it will pave the way for uncharted territory in many areas, including but not limited to: content, procedure, social skills, technical proficiency, ability to solve complex problems, and management of resources. A lot of people don’t realize how quickly computer power is increasing; as a result, jobs in fields as varied as law, finance, surgery, journalism, economics, financial planning, and academia might be automated in the near future [73].

5.7 Solution to the CL-7 (insufficient governmental support)

To successfully adopt Industry 4.0, government backing is crucial. The Bangladeshi government sees Industry 4.0 integration, particularly in the fertilizer sector, as vital to the country’s sustained economic growth. The role of the private sector in the country’s industrialization is growing in importance due to the present status of global competitiveness. The Ministry of Industry officials have stepped into the role of facilitator as a direct result of this. The government has acknowledged the significance of privately owned and run industrial firms as a driving force for economic expansion in response to the problems that are associated with globalization and free markets. That aside, the government has liberalized trade and enacted a slew of favorable reforms, making it easier for entrepreneurs to launch and run successful manufacturing businesses [74].

5.8 Solution to the CL-8 (insufficient management team expertise)

It will be necessary to rethink present employment and skills strategies in order to deal with the challenges given by a world that is becoming more digital. The government should step in to make sure that as the world gets more digital, better jobs are created and that companies and workers are prepared to seize new opportunities when they come [75]. The following are the four important areas that should be the focus of skills policy in order to guarantee that all persons are able to take benefit of these prospects and contribute to comprehensive development:

  1. i.

    Ensuring that all students acquire strong information and communication technology (ICT) skills with strong reading, numeracy, and problem-solving abilities is a part of the obligation.

  2. ii.

    Education and training organizations need to improve their assessment and prediction of changing skill shortages if they want to make changes to the programs and courses they provide and help students achieve good results.

  3. iii.

    Having the right talents for the digital economy isn’t enough; businesses also need to know how to put those abilities to good use if they want to reap the benefits of rising output and competitiveness.

  4. iv.

    Employees need ongoing training to keep up with the ever-evolving skill sets demanded by employers. Companies and workers alike need more motivation to retrain and upgrade their skill sets to meet this challenge [76].

5.9 Solution to the CL-9 (problems of rearranging the production pattern)

It’s a major roadblock to introducing Industry 4.0. An established pattern of production is present in all fertilizer facilities. Industry 4.0 implementation, however, may cause a reorganization of the current pattern. Industry 4.0 compliant manufacturing systems may adapt their configuration on the fly to meet fluctuating production demands, market trends, and system availability. Any firm that wants to be successful must make a significant and costly investment in manufacturing re-configurability [77]. As a direct result of this, scoping tactics are ascending the significance ladder. It is possible to assess the level of re-configurability that the new or updated production system will permit by employing a scoping technique. This method takes into consideration the objectives of the business as well as the amount of capital investment that is required for the deployment of the latest functions and technologies [78].

6 Study’s contribution and conclusions

6.1 Contribution to the theory

In the digital age, the production and logistics systems of a fertilizer manufacturing must be updated to accommodate new technologies. Fertilizer businesses have evolved to become more effective and cost-efficient. Production systems should be customer-centric and encourage business agility. To achieve these goals, Industry 4.0 must be implemented. Bangladesh’s fertilizer industry is well-established and expanding. The industry has emerged as a major contributor to the national economy. According to experts, the rapid expansion of the country’s huge agriculture sectors, as well as major investments in various agro-projects across the country, are driving the growth of the fertilizer industry in Bangladesh. That’s why it’s very important to implement Industry 4.0 in this huge sector. In this sense, it is anticipated that this study will add to the existing reservoir of knowledge by offering a comprehensive analysis of the application of Industry 4.0 in the fertilizer sector, particularly in Bangladesh and the South Asian region. Existing knowledge will also be influenced by the obstacles, their relative importance, and potential solutions.

6.2 Policy recommendation

There are some useful insights in this study for professionals and business practitioners as well. Experts can concentrate on the obstacles that this study has identified. In order to incorporate new technologies and developments, they might take these obstacles into account and try their best to overcome them. Some potential solutions have been suggested in order to aid in the removal of those obstacles. They might also think about using similar remedies to get through their own plant obstacles. Administrators should inspire their staff by emphasizing the importance and benefits of Industry 4.0, as well as providing education on the implementation of new technologies and advances. This study provides valuable insights for policymakers and legislators in the fertilizer sector. This study uncovers intriguing insights on the integration of Industry 4.0 in the commercial fertilizer manufacturing industry. A policy on the implementation of Industry 4.0 in all plants may be introduced. High capital investment was identified as one obstacle. This problem might be resolved by some policy. To ease the process of adoption, some simple lending policies and incentives can be created. In addition, policymakers host some seminars, symposiums, and webinars on the significance and advantages of integrating Industry 4.0 into the plants. If policymakers carefully analyze these all facts and actively pursue the implementation of Industry 4.0, the fertilizer industry of Bangladesh will undoubtedly attain its desired objectives and make a greater contribution to the national economy.

7 Conclusion

This research primarily examines the various issues of implementing Industry 4.0 in the fertilizer sector of Bangladesh. This study consisted of four research inquiries. The authors have thoroughly examined all the inquiries and discovered intriguing results. In order to assess the current state of Industry 4.0 adoption in the fertilizer sector, the author conducted visits and surveys at the top five fertilizer facilities in Bangladesh. Based on a thorough study and expert analysis from five industries, it has been determined that the entire deployment of Industry 4.0 in those factories is less than 50%. In light of the current inadequate installation, the authors proceeded to identify the obstacles and difficulties associated with adopting Industry 4.0. After conducting a thorough analysis of existing literature on the adoption of Industry 4.0, we presented a set of twelve problems to experts. Based on their decision, a total of nine tasks were ultimately chosen for the following phase. The authors gathered data from 10 specialists, consisting of five individuals from the respective businesses and five from prestigious engineering institutes in Bangladesh. They utilized the Best-Worst Method (BWM) to rate the issues. Following an extensive assessment, it was determined that the primary obstacle is a deficiency in understanding Industry 4.0, whereas the least significant barrier is the potential for job loss. Sensitivity analysis was used to confirm the ultimate ranking. Furthermore, it is expected that the sensitivity analysis has confirmed the validity of our findings. Subsequently, detailed suggestions were provided for addressing those nine difficulties. Despite certain limitations in data gathering, this study is expected to make a significant contribution to the fertilizer segment and stimulate a groundbreaking change in fertilizer producing facilities in Bangladesh.

7.1 Limitations and future research direction

All studies, of course, have certain limitations. They’re not above conducting this research, either. Although there are more than thirteen fertilizer industries in Bangladesh, we have only visited the five biggest ones. However, due to time and resource limitations, we are unable to visit other industries. We may additionally gather data for additional academic specialists in order to ensure the caliber of this study. Despite the fact that all participants had adequate training before to data collection, human error could lead to misconceptions of specific individuals during data collection. Additionally, the BWM technique has been the only one applied here. By contrasting our results with those of other MCDM techniques such as the Analytic Hierarchy Process (AHP), TOPSIS, Fuzzy Set Theory, etc., further research might confirm our findings. It is also possible to include more plants and academic experts in further research.