Digital Industry transformation strategy analysis and decision-based on intelligent decision technology

Abstract

Expanding and being competitive in the current economic environment requires companies to embrace digital transformation. In the framework of Industry 4.0, the network of interconnected machines, sensors, and software known as the IIoT plays a crucial role in transforming conventional manufacturing facilities into smart factories, notably in monitoring and optimising the manufacturing process. The issues about enormous record storage and how they react challenge conventional automated methods in the IIoT. Cognitive systems optimally modify production settings based on managing uncertainty and sensory inputs. This work uses the Internet of Things-based decision support system with cognitive automation (IoT-DSS-CA) for industrial informatics across the board, including data collection, transmission, processing, and storage. Incorporating the elements frequently neglected during digital transformation, the suggested method uses the business process management (BPM) paradigm to give a systematic approach that industrial organizations may employ to aid their path towards Industry 4.0. The proposed mechanism is thoroughly investigated and evaluated compared to an original solution using several sensing and decision-making features in industrial parameter settings determined by Simple Additive Weighting (SAW) and Analytic Hierarchy Process (AHP).

Keywords

Businesses Industrial Internet of Things (IIoT)cognitive automation Industry 4.0 business process management

1. Introduction

New and rapidly evolving digital technologies are at the heart of digital transformation, which aims to address previously intractable problems [1]. The term “digitization” is being phased out in favour of “digitization,” which places more emphasis on the “organizational process” or “business process” of technology-induced change within industries, organizations, and marketplaces [2]. Industrial organizations face the transformational influence of digital technology little conceptual and empirical study has investigated how industrial organizations are digitally altered [3]. Better goods and services that provide a competitive edge, improved consumer experiences, innovative business models, and brand-new business processes are all made possible through digital transformation, which combines cutting-edge technological innovations and organizational practices [4]. Most problems when implementing digital methods projects stem from businesses being unprepared for them [5]. Organizational techniques for managing technology and innovation are helpful, but they run into trouble when leaders don’t understand which aspects of the Industry need attention [6]. Decision-making in transit is crucial for autonomous interaction with mission-critical digital infrastructure [7]. To increase productivity, efficiency, and predictability while decreasing the total cost of critical processes, “smart factories” use communication technologies to monitor and collect data in real-time [8].

Managing the production system and making decisions regarding its future is an essential challenge for industrial units [9]. The presence of ambiguity, for instance, will lead to a possible risk of failure for devices and industrial systems, and information gathering for a single problem is often carried out in highly suspicious settings [10]. Understanding the impact of stopping devices and how risk may be managed by monitoring and strengthening the dependability of the control system is one of the most pressing problems in industrial systems [11]. Prior data from the devices’ functions is required to determine dependability, and uncertainties arise if insufficient data is provided [12].

The Industrial Internet of Things (IIoT) is a network-centric notion that employs modern information and communication technologies to ensure industrial adaptability in resource and cost utilization [13]. By adopting these ideas, technology can learn from experience and make decisions based on what it has learned [14]. The major technological advances of Industry 4.0 have made new production methods possible due to the digitization of industrial industries [15]. Industry 4.0’s highly automated production facilities, known as “smart factories,” use cutting-edge technology like AI the IoT and robots to enhance their performance [16]. IoT has been used in smart factories to promote the digital revolution of production and allow businesses to run more efficiently, cheaply, and sustainably [17]. Learning is how a cognitive system acquires and makes sense of information gleaned via perception, reflection, and experience [18]. The development of computing power and Artificial Intelligence (AI) has made it possible to create a computer that can learn as a person does from experience and data [19].

Smart industries collect and analyze vast amounts of information from various sources to provide actionable insights and useful business solutions [20]. Industries that embrace automotive systems benefit greatly from their precision, increased productivity, scalability, cost-effectiveness, and adaptability. The auto industry may also use technology as a subordinate to make decisions based on established patterns and routine operations. Therefore, the processing and integration of all of an enterprise’s data may lead to cognitive capabilities in those sectors.

This study uses the IoT-DSS-CA framework to manage and secure the transmitted data using SAW and the AHP. To analyze the reaction or behaviour of each device concerning specified criteria, SAW is generally accepted as the most effective and extensively utilized approach. Applying the SAW decision-making model to evaluate a communication node’s characteristics yields an estimate of that node’s reliability. SAW is also used with the AHP method to simplify poorly organized issues by providing a hierarchical decision-making framework.

The main contribution of this paper is as follows:

i)
Designing the proposed IoT-DSS-CA that can monitor and adjust its local environment based on acquired data.
ii)
Cognitive automation may help improve decision-making when using SAW and AHP methods.
iii)
The numerical results have been performed, and execution time, correctness of records, analysis of stored data, and resource consumption are only a few of the metrics that may be used to evaluate the suggested cognitive mechanism.

There is a literature review in Section 2, a discussion of the suggested technique in Section 3, numerical findings and discussion in Section 4, and a summary and conclusion in Section 5.
2. Related work

The sequential steps manufacturers take to adopt the technologies that make possible Industry 4.0 [21]. The expanded foundation on Industry 4.0 enabling technologies that this study helps to create will help researchers better understand and implement smart manufacturing. This study suggests that a company’s internal infrastructure must be fully connected to use Industry 4.0’s enabling technologies. Companies vary in their preparedness, which reflects the gaps and hurdles they face as they work to change to an Industry 4.0 (I4.0) state in the face of mounting demand to reinvigorate industrial sectors with Smart industrial capacity within the I4.0 framework [22]. Systematic literature research (SLR) will systematically examine and choose the most relevant studies on the major aspects of the 14.0 maturity models. People, society, organizational resources, computer systems, business processes, technologically advanced commodities, and services are all elements that will be evaluated consistently to define a fully developed market. Data-driven process monitoring and problem diagnostics face new difficulties in the age of Industry 4.0 as highly complex production equipment becomes more interconnected and intelligent [23]. When managing the flow of information inside an industry, cloud computing and big data storage significantly improve real-time failure diagnostic (RTFD) technology. This study thoroughly examines recent RTFD developments in machine condition tracking and manufacturing process management.

Industry 4.0’s emergence has far-reaching consequences for business, culture, and other production areas since it uses cutting-edge, high-intelligence technology [24]. This article outlines the steps taken to create a cyber-physical system that is compliant with the innovative smart factory framework (ISMF) for Industry 4.0 and capable of using its foundational industrial, computer, information, and communication technologies. There’s advice on how to build an intelligent production system out of the several “pillars” that make up a smart factory. The study’s Digital life cycle management (DLCM) framework aims to assess the existing status of digitalization in EIIs and to facilitate the implementation of sustainable smart manufacturing in this sector [25]. The goals of this work are threefold: (i) to gain insight into the potential sustainability benefits of process mining and simulation modelling; (ii) to lay the groundwork for the digital transformation of EIIs; and (iii) to embed knowledge in EIIs to boost their energy and material efficiency. The suggested 5-tiered system incorporates data collecting, process management, simulation & modelling, intelligent technologies, and data visualization to track and forecast future energy use. In recent years, the industry has used smart systems, machine learning (ML) inside AI, and predictive maintenance (PdM) approaches to monitor the condition of their machinery [26]. This paper aims to provide a thorough overview of the most recent advancements in ML techniques widely applied to PdM for smart manufacturing in IoT. To do so, we will classify the research findings according to the machine learning (ML) algorithms, ML category, equipment, and electronic equipment used, the tools used in gathering knowledge, the categories of data, dimensions, and types, and finally, we will highlight the most significant achievements of the researchers and provide standards and the basis intelligent manufacturing.

The IIoT refers to integrating IoT technologies into traditional manufacturing processes to create smart products. Intelligent connectivity, real-time data processing, collaborative monitoring, and autonomous information processing are all necessary for IIoT to improve product quality and dependability [27]. This study presents a Digital Twin (DT) and Blockchain (BC)-based automated smart manufacturing framework (SMF-DT-BC). All of the DT’s input data comes from a cluster that was verified via blockchain. As simulations show, the suggested authentication technique is faster than the current approach. Our DT framework for a smart factory also employs the PDQN DRL model, which is superior in precision, consistency, and dependability. Capability frameworks of the smart manufacturing (SM) industries involved in the IIoT and proposes conceptual and capability frameworks (CCF) for the IIoT’s underlying infrastructure [28]. These models depict the connections between IIoT and SM, including the intricate webs of interdependence among the foundational nodes of IIoT’s physical infrastructure. This article examines the IoT layers used in an IIoT architecture and assesses their commonalities and requirements at a high level. In light of these assessments, lay forth the capability framework and crucial competencies of IIoT infrastructure. Smart manufacturing is a growing movement toward better production decisions and increased productivity. Therefore, Dataspace is deemed a practical and efficient approach in this paper [29]. The contribution is a new industrial Dataspace architecture for smart manufacturing that is static in its organization and has functional flow pathways. The proposed Industrial Dataspace Platform (IDP) combines the smartness of pay-as-you-go with the distinctive features of smart manufacturing. These include, but are not limited to, using distributed heterogeneous data from manufacturing companies; using a body of knowledge (or ontology) to make sense of business data, linking that data to smart software, and releasing corresponding decisions.

3. Proposed methodology

The proposed IoT-DSS-CA paradigm relies on cognition-automated (CA) Internet of Things (IoT) devices that can sense and control their local environs using data collected from various sources and make good decisions based on this information. It’s predicated on the premise that a typical industrial setup consists of various autonomous units and sub-entities, each performing a specific function. Coordinating these operations requires a significant investment of time and labor. However, deploying several IoT nodes throughout an industry or factory for data collecting and autonomous decision-making may make management substantially more productive and cost-efficient.

Figure 1.

Internet of Things based Industry 4.0 Digital Transformation.

The primary possibilities, tools, and problems associated with industry 4.0-enabled digital transformation in influencing company strategy are summarized in Fig. 1. The next section addresses this more. The rise of a knowledge-based economy is one of the most meaningful changes of the last few decades. The digitization of businesses, organizations, and institutions is linked to developing a knowledge economy. Information and expertise are the primary means of differentiation in a knowledge-based economy. Success in the digitalized knowledge-driven economy requires an exceptional capacity to acquire, interpret, and apply data from the digital world. Much literature demonstrates the merits of such (digital) capabilities. Digitalization actively promotes the expansion of new businesses and start-ups since it lowers the barriers to entry for doing so. Digitalizing a company is ideal for a startup to expand worldwide. Knowledge, digital servitization, and leadership are only a few examples of human and non-human factors that were found to affect internationalization on micro, meso, and macro scales. The positive effects of digitalization on export performance were verified. One of the most significant post-pandemic trends in the growth of small and medium-sized firms is the increasing prevalence of digitalization. The new competitive environment is being ushered in due to digitalization’s impact on company strategies, models, and procedures. The goal of digitalization is to cut down on the waste of resources used in corporate operations; nonetheless, digitalization often leads to a major shift in business models. Existing businesses may benefit much from digital transformation in terms of innovation, particularly in the face of substantial market and technical uncertainty. Increased visibility, more accurate forecasts, better adaptability, and real-time cooperation throughout the whole supply chain are all possible outcomes of digital technologies like IoT, which increase demand responsiveness and capacity flexibility. Since these businesses will exist mostly online, they will differ from conventional ones. A storefront is not a problem for these companies since their clients create and consume value digitally.

With cyber-physical structures enabling computerized organizational/institutional activities in intelligent manufacturing facilities, Industry 4.0 perfectly defines the basics of the Fourth Industrial rebellion, with significant repercussions for realistic expenditures, intake, growth, opportunities for employment, and the marketplace. It’s a new way of managing and controlling every step of the value chain for specialized goods and services that suit diverse customer tastes. Customized products, computerized ordering, automatic data dissemination, and unified support systems are all possible thanks to advances in digitalization and the integration of value chains horizontally and vertically. Complete smart grid descriptions of items and services are made accessible via digitalization. Innovative plans for doing business online are being implemented. High degrees of system integration and technological capability for establishing unique, compatible electronic business models. The backbone of the industrial internet is the ability to access and monitor embedded devices in real time throughout a whole enterprise.

Figure 2.

Business process management for industrial digital transformation.

Figure 2 depicts the BPM framework and its accompanying stages. Organizations in the manufacturing sector may utilize the suggested BPM framework to enact digital transformation in line with Industry 4.0 technology.

3.1 Process identification

An interdisciplinary group consisting of managers, process owners, individuals, economists, and system designers is formed at the commencement of the BPM framework. The goal of assembling such a broad group was to increase the likelihood that a unified strategy would isolate the processes at the heart of the issue. Existing methods often fail because the implementation team fails to recognize the interdependencies among several aspects that affect operations and how smoothly they execute. An efficient BPM team would aid in isolating the key elements and drawing connections between them. Outlining the processes needing fixing or reengineering and creating a measurable KPI for success is essential. One of the fundamental tenets of BPM is strategic alignment, which is essential for gaining buy-in from upper management and minimizing pushback from employees.

3.2 Process discovery and process analysis

Business process discovery is the first step in improving or reengineering a business process. Understanding the interdependence of several business processes requires defining where each begins and ends and the interfaces between them. It is crucial that the process flow chart’s data, linkages, sequence movements, and decision points can be trusted. A value stream map might be helpful to understand better how a company’s resources and data move through a process from beginning to conclusion. To achieve this goal, a process model must be developed that facilitates open dialogue and comprehension among all parties involved. The Business Process Management group may then analyze the process flow and identify areas for improvement.

3.3 Process redesign and reengineering

The BPM group’s study should inspire fresh, original idea designs. Choosing between redesign and re-engineered design calls for a careful examination of the benefits and drawbacks of each approach. The BPM group should brainstorm and talk about potential new process designs, but they must additionally consider how those changes could affect other parts of the business. The BPM team has to weigh the organization’s long-term goals and available resources while deciding between process redesign and reengineering. A gradual or drastic shift towards digital transformation will need careful consideration of the available choices; nonetheless, the key will be to define the process in a way consistent with the strategic needs of the business.

3.4 Streamlining business process

Both redesigning and reengineering a process may affect the results of a company’s operation. Therefore, other business processes must be streamlined to guarantee they can continue to function without interference. In any case, the ’to-be’ process may alter how various business processes (or departments) rely on one another. The BPM group must take into account these changes and make appropriate preparations. It was a rebuilt or re-engineered process model, but either way, it should be harmonized with related business procedures for optimal efficiency. If we perceive manufacturing as a process that requires our focus, increasing our ability to produce goods should lead to increased sales. The sales team will have to work harder to meet the increased demand. The need for raw materials and the quantity of stock on hand will rise. There will be a dramatic rise in the company’s cash inflow and outflow. Not many companies can withstand such a sudden spike since they lack the agility and flexibility in their operations. The BPM team must alter the process for the entire company to accommodate these changes. The team must analyze the process thoroughly, assess the results using simulation models, and get input from team members and other individuals if it is to account for every possible outcome and its potential repercussions. Identifying, assessing, and mitigating the effect on interrelated business operations is best accomplished via an open, cross-departmental collaborative effort. The BPM team may undertake cross-departmental dependency studies to better understand the interdependencies and independence of the activities.

3.5 Risk management and contingency planning

The BPM group should include all four categories of risk management: prevention, reduction, transfer, and acceptance. Information gathering, risk assessment, and risk treatment all need risk intelligence. Methods such as brainstorming (a group exercise for generating many ideas), premortem (imagining failure and identifying the likely causes), and counterfactual thinking (considering alternative courses of action) might help identify potential dangers. Creating a risk register, which involves assessing the likelihood and effect of potential risks, is a risk analysis. It is common practice to document potential threats to a project, together with their associated levels of probability and impact and the steps that will be taken to lessen or eliminate them. The BPM team might utilize this technique to monitor the potential and impact of their input on digital transformation. Compare that with contingency planning, which focuses on what to do if and only if something terrible occurs. Both are critical to the success and safety of the business. Knowing what to do in a hazard is an essential aspect of risk minimization. As a result, it makes sense to include contingency planning with other forms of risk management. Since the organization is taking a leap of faith with Industry 4.0, contingency plans must be developed and communicated to employees.

3.6 Skill gap analysis

Concerns about Industry 4.0 stem from its nebulous definition, unclear implementation, and questionable organizational readiness. Efforts have been made to cope with such ambiguities, and one such effort is known as Work 4.0. This broad term includes the potential benefits and drawbacks of the widespread use of the enabling technologies of Industry 4.0. The issues of Industry 4.0 and the human-machine interface are highlighted by the concept of “Work 4.0.”s This raises serious concerns regarding job losses, skills deterioration, and labour intensity in light of the company’s technological requirements. Given this challenge, it is essential to put resources toward developing skills and expanding possibilities for growth at an early age. Guaranteeing that all employees can contribute to value creation requires a company-wide effort to enhance the skills not only of low-skilled labourers but also those directly responsible for technical tasks. Because of this discrepancy, the BPM team should do a skills gap study to determine what training is necessary to help the company reach its goals. Initial interviewees for the inquiry should be those working on the company’s process under inspection. Companies have problems training their people with the knowledge and competencies essential to thrive in the new Industry 4.0 setting.

3.7 Change management

Since the transition to digital transformation is so radical, there will inevitably be pushback from employees who are worried about losing their jobs, won’t get promoted quickly, or refuse to abandon their established work habits. The BPM team must carefully manage the transition to Industry 4.0, with the workforce as its primary emphasis. Adapting to a new situation highlights how complex human beings can be. To keep talented employees from leaving during times of transition, all departments must participate in change management. The BPM group should provide an official case for the change, including its reasons, its effects, and how it ties in with the organization’s long-term objectives.

3.8 Cost benefit

The difficulty in accurately predicting the benefits of implementing Industry 4.0 is an important obstacle to the wider adoption of the technology. Because most managers don’t understand what Industry 4.0 technologies can achieve or how they may help their company, converting advantages to monetary value is incredibly difficult. The answer depends heavily on the scope of the company’s investigation; more time and manpower will be required for a more in-depth study. The BPM group now must calculate the monetary worth of all potential advantages of embracing Industry 4.0 enabling technology and compare that figure to the expenses of doing so. It is more common for individuals to underestimate expenses than to overestimate rewards. The external Industry 4.0 specialists engaged by the BPM team to guarantee proper benefit identification, using process owners to quantify benefits and debate implementation costs. If the BPM group is serious about maximizing ROI, they should use a tried-and-true methodology to analyze their acquired data. The Phillips ROI Methodology TM is one such strategy consisting of ten steps over four stages. The first stage is evaluation planning, which entails formulating project goals and an assessment strategy. The second stage, data collection, includes gathering information before and after implementing the initiative. Isolating project impacts, recording project expenses, transforming data to monetary value, calculating return on investment, payback period, or benefit-cost ratio, and ultimately finding intangible advantages all make up data analysis’s third and most difficult phase. In the third stage, known as reporting, results from the implementation are generated and shared with relevant parties (both internal and external).

3.9 Process validation and process implementation

The BPM group has to figure out what’s helping and hurting the process of going all-in, and they need to understand the consequences of ignoring the risks. Further, a cost-benefit analysis should be performed to determine whether or not a company-wide rollout would be economically viable.

3.10 Process monitoring

The business process has been implemented and is live; the BPM team should collect the necessary information to conduct a post-implementation evaluation of the process to identify areas for future enhancement. The collected data must be examined to evaluate the new process’s success in meeting its performance metrics and goals. Common problems in production systems include bottlenecks, faults, and deviations, all of which should be detected and addressed. The monitoring and assessment results should be disseminated across the enterprise to feed into the iterative BPM improvement lifecycle. The Business Process Management (BPM) cycle may need to be repeated if and when new problems appear in the same or other business processes.

3.11 Process controlling

In this stage, the BPM team should concentrate on process performance, process management, and overall business success. Process monitoring and control is an ongoing endeavour that should be implemented to ensure that all aspects of a company’s operations are being optimized.

Figure 3.

Cognitive automation based IIoT.

Efforts to expedite industrial growth and automate information and control techniques to eliminate the need for human contact in production and other activities led to the creation of a new intelligent technology termed cognitive automation. Cognitive automation has developed an effective method of adjusting their behaviour in response to uncertainty management and sensory input, allowing them to take charge of IIoT industrial environments. However, businesses do not fully use cognitive systems because of inadequate IT integration. They can increase dependability and safety in transportation, IoT, and other vital infrastructure components. As shown in Fig. 3, entities in a decentralized IIoT architecture must use cognitive systems to communicate a wide range of documents, information, and items. The goal of cognitive automation in the Industrial Internet of Things is to create a system that can learn from its environment and modify its behaviour accordingly. All industrial activity is legally recorded and stored, but occasionally this equipment may be hacked by intruders. To resolve these problems, we need a reliable detection system capable of determining whether or not to identify potentially dangerous gadgets and to halt their activity.

Sensitive data might be stored in a smart factory’s database and accessed by hackers. From the perspective of smart factories, business process management (BPM) is also critical for establishing production linkages and data analysis to provide insight for making decisions. For BPM and IIoT automation to continue working smoothly, decision-makers must consider social, economic, and environmental issues. There has been a recent uptick in interest in IIoT for automotive robots due to their decreasing cost and enhanced management abilities. Robotic systems assist in making IoT-DSS-CA’s various features useful for managing and implementing the optimization of manufacturing and production. In addition to less trustworthy techniques, these approaches may be applied in healthcare, supply chain assessment, green choice of suppliers, and infrastructure for transport decision-making. We need cognitive automation that can read and analyze vast amounts of sensory input, make intelligent decisions based on that data, and then regulate the environment.

Figure 4.

Proposed IoT-DSS-CA.

The suggested paradigm is built on cognition-automated Internet of Things (IoT) devices that can acquire data from various sources, process it, and then make effective decisions based on what they learn about their local environment. A standard manufacturing facility is divided into smaller sub-entities called “units,” each performing a specific function. Coordinating all of these steps is not only time-consuming physically demanding. An industry or factory operation may be considerably more efficient and cost-effective when several IoT-enabled nodes are deployed around the organization to gather data and make decisions automatically. This part aims to decide appropriate responses to events, objects, situations, and scenarios. This paper describes the suggested cognitive model for industrial informatics, which combines the SAW and AHP models. In this subsection, we suggest an AHP-integrated SAW mechanism in which IoT-based entities perform environmental sensing, data generation, and report submission. It’s a consolidated system for objectively evaluating businesses’ operations, output, and access to necessary resources. In CAs, smart sensors collect data for analysis using IoT-based node-level decision-making variables. CA reviews the supplied reports and evaluates each company’s service regarding response time, processing efficiency, and record keeping. The suggested framework uses SAW combined with AHP to compare the quality of service provided by various organizations. The CA-IoT architecture is shown in Fig. 4 in a real-time situation where it is necessary to log communications and transmit data via a trusted party. In addition, CA has hired SAW to keep an eye on the stats related to the service. According to the completed CA report, the criteria are tracked and the services are assessed.

The suggested framework may be formulated as a decision-making system for resource management, record handling, and efficiency assessment. The three main parts of this decision-making model – the CA, the SAW, and the AHP – are shown hierarchically in Fig. 4. The CA must track how many variables influence the device’s dependability. The AHP technique is included in the SAW approach as a means of helping the decision-maker reliably ascertain the importance of certain attributes. Concerns about making a good decision may be neatly partitioned into constitutional portions using AHP’s strong methods for evaluating the consistency of assessment alternatives and measurements.

The suggested CA makes use of enhanced SAW for entity trust formation. $B=\left\{{\left.{b_{j}}\right|b_{1},b_{2},\ldots,b_{m}}\right\}$ is a collection of choices, $d=\left\{{\left.{d_{j}}\right|d_{1},d_{2},\ldots,d_{m}}\right\}$ is a set of criteria for choosing the best option, and $z=\left\{{\left.{z_{j}}\right|z_{1},z_{2},\ldots,z_{m}}\right\}$ is a set of weights. What follows is a discussion of the primary tactics used by the CA.

Phase 1:

To begin build an assessment matrix with $n$ entities/devices (options) and $t$ services. The first thing to do in SAW is to build a decision matrix, written as $c=\left\{{\left\{{C_{ji}}\right\}}\right\}:j=\left[{1:m}\right],i=\left[{1,m}\right]$ in which each cell represents an option and each row represents a criterion.

Phase 2: Additionally, a normalized decision matrix for advantageous and disadvantageous traits, known as:

$\displaystyle\textit{Beneficial Attiribute}=\frac{c_{ji}}{c_{ji}^{\max}}$ (1) $\displaystyle\textit{Non -- beneficial Attiribute}=\frac{c_{ji}^{\min}}{c_{ji}}$ (2)

Equations (1) and (2) show that normalized decision-making matrix beneficial attribute and non-beneficial have been calculated for high accuracy. Wherein $c_{ji}^{\max}$ is the highest gadget evaluation value associated with a substitute ai and criterion $c_{ji}$ and $c_{ji}^{\min}$ is the lowest gadget rating value associated with alternatives $b_{j}$ and criteria $d_{i}$ .

Phase 3: For each service parameter need to assign a weight $z_{ji}$ (to each choice and criterion) using a weighted normalized decision matrix like as:

$\displaystyle U_{ji}=Z_{ji}\times O_{ji},\sum_{j=1}^{m}Z_{i}=1$ (3)

Equation (3) shows that the data transmission rate is improved when the decision matrix is weighted and normalized. Here, the AHP method is presented in the improved SAW to help balance the relative importance of service criteria methodically. The following details the criteria by which the AHP weights are evaluated. Make a comparison matrix of two services using the relative importance of the criteria you’ve chosen to evaluate them. Considering that there are $t$ service parameters may create a square matrix $t_{m\times m}$ where $t_{ji}$ indicates the weight given to option $j$ based on criterion $i$ .

$\displaystyle t_{m\times m}={\begin{array}[]{c}{t_{1}}\\ {t{}_{2}}\\ \vdots\\ {t_{m}}\hfill\\ \end{array}}\left|{{\begin{array}[]{ccccc}1&{t_{2}}&\ldots&\ldots&{t_{1m}}\\ {t_{2}1}&1&\ldots&\ldots&{t_{2m}}\\ \vdots&\vdots&\vdots&\vdots&\vdots\\ {t_{m1}}&{t_{m2}}&\ldots&\ldots&1\\ \end{array}}}\right|$ (4)

As obtained in Eq. (4), the square matrix has been discussed to achieve better decision-making. After calculating the methodical mean of the $j$ th row and the normalized geometric average of the rows in the two-dimensional matrix, service weights ( $Z_{i}$ ) are generated for use in the comparison.

The normalized service (MT) matrix is found by validating the Wi value of each service parameter and then using the formula $mt_{m\times 1}=t_{m\times m}\times z_{m\times 1}$ . The relative normalized service (RNS) matrix is calculated as $\textit{RNS}_{m\times 1}=\textit{MT}_{m\times 1}/z_{m\times 1}$ .

Additionally, the largest eigenvalues $F_{\max}$ , are determined. To put it simply, $F_{\max}$ is the mean of the $\textit{RNS}_{m\times 1}$ matrices. Calculate the Consistency Index $\left({DJ}\right)$ by dividing $\textit{RNS}s_{m\times 1}/\left({m-1}\right)$ . Random index $\left({OJ}\right)$ is based on the total number of characteristics and used to determine the consistency ratio, which we write as $DJ/OJ$ .

Phase 4: The ranking of the $j$ th option is calculated by:

$\displaystyle td_{j}=\sum{i=1}^{m}U_{ji},j=1,2,3,\ldots m$ (5)

Phase 5: Where $t_{j}$ is the matrix score from a set of alternatives $j$ , and $m$ the number of alternatives to improve the higher efficiency. The preceding steps are the reasoning behind analyzing the ultimate optimal service from several angles.

This research uses Simple Additive Weighting (SAW) and Analytic Hierarchy procedure (AHP) to build an innovative and effective decision-making procedure for managing and regulating the transmitted information. To analyze the reaction or behaviour of each device about a set of parameters, SAW is by far the most popular and effective approach because of its ability to efficiently get a weighted total performance rating of each alternative’s overall qualities. Each communication node’s credibility is calculated using the SAW decision-making model, which considers the node’s unique properties. In addition, SAW is used with the AHP technique to reduce unstructured problems via the hierarchical organization of decision-making components. By isolating a node’s positive and negative qualities, AHP helps to refine the calculation of its credibility.

4. Numerical analysis

Adapting to Industry 4.0 is now a need. Companies, in the present day, cannot settle with mediocrity. This has prompted serious thought about when and how to implement digital transformation. Business process management (BPM) might help manufacturers jump from old methods to those of Industry 4.0. To help businesses reduce their failure risk, the framework has integrated under-researched problems. Since manufacturers have been using business process management for the better of a decade, they are already very acquainted with them. The suggested approach is predicated on IoT-DSS-CA devices with local awareness that can perceive and regulate their environments using data from various sources. Most factories are divided into smaller portions called “units,” and each unit is in charge of a certain function. Coordination of all of these tasks is time-consuming and labour-intensive. However, when several IoT-enabled nodes are dispersed around an industry or factory to receive data and make automated choices, management and efficiency are maximized.

We assess and compare the performance of the suggested mechanism to that of the baseline approach using several factors to verify our findings. The suggested model is tested in stages, with increasing devices in each iteration. We consider 100 IoT devices spread out evenly over the observation region, with a 60-second interval between updates to the decision-making criteria. Service behaviours like ideal, malevolent, high energy consumption, super active, etc. classify IoT devices into several kinds. Numerical simulation in MATLAB is used to investigate the suggested phenomena and demonstrate the efficacy and control of data transmission in IIoT. The rest of the simulation settings are shown in Table 1.

Table 1
Simulation parameters

Parameters	Setting
Coverage Range	[450 450]m
IoT Device	[45 160]km/h
Weight Parameters	(20 40)
Bandwidth	18 Mhz
Block Size	[50 640]KB

4.1 Efficiency ratio (%)

The IIoT is a new subset of IoT gaining traction in manufacturing facilities because it enables real-time data monitoring and access from any location. An intruder might potentially compromise important information contained in the smart factory database. In addition, from the point of view of smart factories, a BPM is vital to establish communication between the various stages of production to provide information for making decisions. When making decisions on BPM and IIoT, it’s important to keep social, economic, and environmental factors in mind to ensure everything runs well. Due to their decreased price and improved management skills, automotive robots have recently garnered much interest in IIoT.

Figure 5.

Efficiency ratio (%).

Figure 5 depicts the efficiency ratio. With the help of robotic systems, the many aspects of DSS allow for effective management and implementation of production and manufacturing optimization based on Eq. (5). Based on this knowledge, a cognitive automation (CA) that can extract data and regulate the environment is necessary to understand and process massive amounts of sensory input and make intelligent decisions.

Figure 6.

Accuracy ratio (%).

4.2 Accuracy ratio (%)

The precision ratio is examined in Fig. 6. Based on Eqs (1) and (2), we examine how the hypothesized phenomenon affects the efficiency of decision-making processes in diverse industries. Figure 6 illustrates how precise and effective the automated judgments of each node are. Cognitive systems ensure superior performance by considering a wider range of evaluation criteria. Significant progress has been achieved because of SAW and AHP methodologies, which evaluate the quality of service by contrasting the precision with which IoT entities are created, recorded, and analyzed. The cognitive system’s efficiency comes from the fact that it checks and verifies each IoT gadget using various criteria for making judgments. An AHP or SAW-based cognitive automation system continuously assesses validity, efficiency, and resource utilization.

4.3 Data transmission rate (%)

Data corruption is shown in Fig. 7 when potentially hostile IoT devices gather or store records. The predicted phenomena outperform more conventional techniques for detecting data modifications. The SAW technique is used first to classify each device’s attributes that establish the node’s validity, which is the root cause. In addition, the AHP technique filters out the changed or poorly formatted records from the data collected by IoT gadgets.

Figure 7.

Data transmission ratio (%).

Increased hardware facilitates joint SAW/AHP identification of the altered record. Based on Eq. (3), examining each person’s behaviours takes longer than maintaining the current status quo. The justification for this is that additional upkeep or measurement parameters are needed to verify the accuracy of the node. The SAW method uses measurements of service characteristics to analyze the mental state of individual nodes. However, the SAW algorithm is combined with the AHP method to speed up each node’s analysis or storage process.

4.4 Decision making ratio (%)

Internet of Things (IoT) devices and sensors are utilized in smart factories to collect data about the manufacturing process as a whole, which is then used to make informed choices in real time. Data is collected and analyzed, algorithms are implemented, processes are monitored in real time, and human experience is used to make choices in IoT-enabled smart factories. To guarantee seamless data transmission and data exchange across network entities have presented a unique and effective decision-making procedure in this study.

Figure 8.

Decision-making ratio (%).

Figure 8 explains the decision-making ratio. The suggested approach utilizes the SAW technique to categorize the important characteristics, allowing sensors to effectively control data transport over the Internet. In addition, SAW is used in collaboration with the AHP technique to streamline complex problems by prioritising the most important considerations based on Eq. (5). Using SAW and the AHP mechanism, the suggested phenomena may swiftly and accurately perceive the transferred data. Furthermore, the mechanism is thoroughly evaluated across a wide range of assessment criteria, including but not limited to data transmission, data correctness, shared data time, and sensing time. Better economic results may be achieved when manufacturing facilities use IoT technology to enhance operations and boost productivity.

4.5 Data protection analysis (%)

The suggested method may be assessed in terms of its efficacy using AHP and SAW by simulating a scenario in which hostile actors attempt to undermine the communication mechanism or tamper with data before delivering it to other devices. Typically, stake-based voting techniques are used to choose trustworthy entities in an adversarial framework before launching the framework. Some organizations are spread out without adequate security, making them vulnerable to attacks since less-trusted entities may compromise them.

Figure 9.

Data protection ratio (%).

Figure 9 shows the data protection ratio. The attackers penetrate many devices to generate and submit false information to FC regarding the availability of channels; this attack is known as false report generation (FRG).

$\displaystyle\textit{FRG}=\sum_{y=1}^{n}\frac{\textit{Altered reports}}{% \textit{Total Generated reports}}$

The goal of this kind of cyber assault is to turn off a device to stymie some form of communication temporarily. This is done to enable safe communication and frequency sensing. Attackers may use denial-of-service attacks or exploit IoTs to send false data to the CM.

The proposed IoT-DSS-CA model is highly adaptable, makes real-time decisions, and acquires contextual information from IoT sensors compared to other methods, such as fuzzy logic and machine learning. A decision-based strategy relies on predetermined criteria and expert knowledge to conclude. Problems where decision-making rules are readily expressed are good matches for solutions. However, in machine learning, algorithms are trained on data to discover patterns and then make decisions or predictions without explicit programming. As time passes, ML algorithms better manage complicated and unstructured data, find hidden patterns, and adjust to new conditions. For decisions when accuracy is lacking, fuzzy logic provides a mathematical framework that can accommodate imprecise language and abstract ideas. However, AI-driven decision support systems, optimization algorithms, expert systems, rule-based systems, and other approaches are all part of intelligent decision technology. It combines multiple AI approaches to improve decision-making processes in the industrial digital transformation. The machine learning algorithm’s limitations include data dependency, overfitting and computational complexity. On the other hand, fuzzy logic has complexity in rule development and limited precision and scaling.

5. Conclusions

In the framework of Industry 4.0, the network of linked equipment, sensors, and applications known as the Industrial Internet of Things (IIoT) plays a crucial role, notably in monitoring and optimizing the production process in transforming conventional factories into smart factories. Conventional automated approaches in the IIoT are challenged by difficulties related to massive record storage and how they respond. Controlling production settings accurately requires cognitive systems to effectively adapt their behaviours depending on handling uncertainty and sensory input. This study combines the IoT-DSS-CA for corporate computing applications across the board, including data collection, transmission, processing, and storage. The proposed strategy employs the business process management (BPM) paradigm to provide a systematic approach that industrial companies may adopt to help them along the route towards Industry 4.0, and it considers the factors often overlooked during digital transformation. The proposed mechanism is compared to an original solution using numerical analysis and industrial parameter settings emphasising various sensing and decision-making features.

Funding

The logical and practical path of digital enabling the high-quality development of Fujian cultural tourism industry. Natural Science Foundation Project of Fujian Province, funded by: Science and Technology Department of Fujian Province, 2023. Number: 2023j05213.

Data availability statement

Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

Footnotes

Conflict of interest

The authors declare that there is no conflict of interest with any financial organizations regarding the material reported in this manuscript.

References

Ostmeier

Strobel

. Building skills in the context of digital transformation: How industry digital maturity drives proactive skill development. Journal of Business Research. 2022; 139: 718-730.

Zhang

Shi

Chen

. Enterprise digital transformation and production efficiency: Mechanism analysis and empirical research. Economic research-Ekonomska istraživanja. 2022; 35(1): 2781-2792.

Ghosh

Hughes

Hodgkinson

Hughes

. Digital transformation of industrial businesses: A dynamic capability approach. Technovation. 2022; 113: 102414.

Chen

Sun

Chen

. Digital transformation, labour share, and industrial heterogeneity. Journal of Innovation & Knowledge. 2022; 7(2): 100173.

Pauliuk

Koslowski

Madhu

Schulte

Kilchert

. Co-design of digital transformation and sustainable development strategies-What socio-metabolic and industrial ecology research can contribute. Journal of Cleaner Production. 2022; 343: 130997.

Suyunovich

Shakhriyorovich

. Innovative Development Mechanism of Digital Transformation Processes in Regional Industry. Journal of Pharmaceutical Negative Results. 2022; 492-502.

Wang

Tian

. Digital transformation and electricity consumption: Evidence from the Broadband China pilot policy. Energy Economics. 2022; 115: 106346.

Tavana

Shaabani

Raeesi

Kumar

. A review of digital transformation on supply chain process management using text mining. Processes. 2022; 10(5): 842.

Van

Trincado

Rubin

Vorley

. Rethinking the digital transformation in knowledge-intensive services: A technology space analysis. Technological Forecasting and Social Change. 2022; 179: 121631.

10.

Zhang

Jin

Zhang

. Digital transformation of incumbent firms from the perspective of portfolios of innovation. Technology in Society. 2023; 72: 102149.

11.

Wang

Yang

Wang

Han

. Chat with chatgpt on industry 5.0: Learning and decision-making for intelligent industries. IEEE/CAA Journal of Automatica Sinica. 2023; 10(4): 831-834.

12.

Nayeri

Sazvar

Heydari

. Towards a responsive supply chain based on the industry 5.0 dimensions: A novel decision-making method. Expert Systems with Applications. 2023; 213: 119267.

13.

Yang

Gai

Cao

Zhang

. Application of group decision making in shipping industry 4.0: Bibliometric Analysis, Trends, and Future Directions. Systems. 2023; 11(2): 69.

14.

Saha

Pamucar

Gorcun

Mishra

. Warehouse site selection for the automotive industry using a fermatean fuzzy-based decision-making approach. Expert Systems with Applications. 2023; 211: 118497.

15.

Yang

Gai

Cao

Zhang

. Application of group decision making in shipping industry 4.0: Bibliometric Analysis, Trends, and Future Directions. Systems. 2023; 11(2): 69.

16.

Qahtan

Alsattar

Zaidan

Deveci

Pamucar

. Performance assessment of sustainable transportation in the shipping industry using a q-rung orthopair fuzzy rough sets-based decision making methodology. Expert Systems with Applications. 2023; 223: 119958.

17.

Zayat

Kilic

Yalcin

Zaim

. Application of MADM methods in Industry 40: A literature review. Computers & Industrial Engineering. 2023; p. 109075.

18.

Baninemeh

Farshidi

Jansen

. A decision model for decentralized autonomous organization platform selection: Three industry case studies. Blockchain: Research and Applications. 2023; 4(2): 100127.

19.

Zhang

Wang

Zhou

Chang

Jing

Zhao

. Towards new-generation human-centric smart manufacturing in Industry 50: A systematic review. Advanced Engineering Informatics. 2023; 57: 102121.

20.

Singh

Madaan

Kumar

. Smart manufacturing systems: a futuristics roadmap towards application of industry 4.0 technologies. International Journal of Computer Integrated Manufacturing. 2023; 36(3): 411-428.

21.

Ejaz

. Implementation of industry 4.0 enabling technologies from smart manufacturing perspective. Journal of Industrial Integration and Management. 2023; 8(2): 149-173.

22.

Susanto

Simatupang

Wasesa

. Industry 40 Maturity Models to Support Smart Manufacturing Transformation: A Systematic Literature Review. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi). 2023; 7(2): 334-344.

23.

Yan

Wang

Zhou

Peng

. A Review of Real-Time Fault Diagnosis Methods for Industrial Smart Manufacturing. Processes. 2023; 11(2): 369.

24.

Ryalat

ElMoaqet

AlFaouri

. Design of a smart factory based on cyber-physical systems and Internet of Things towards Industry 40.. Applied Sciences. 2023; 13(4): 2156.

25.

Chinnathai

Alkan

. A digital life-cycle management framework for sustainable smart manufacturing in energy-intensive industries. Journal of Cleaner Production. 2023; 419: 138259.

26.

Çınar

Abdussalam

Zeeshan

Korhan

Asmael

Safaei

. Machine learning in predictive maintenance towards sustainable smart manufacturing in industry 4.0. Sustainability. 2020; 12(19): 8211.

27.

Chen

Jeremiah

Lee

Park

. A Digital Twin-Based Heuristic Multi-Cooperation Scheduling Framework for Smart Manufacturing in IIoT Environment. Applied Sciences. 2023; 13(3): 1440.

28.

Zhang

Huang

Tian

Sang

. Framework and Capability of Industrial IoT Infrastructure for Smart Manufacturing. Standards. 2023; 3(1): 1-18.

29.

Guo

Cheng

Wang

Tao

Pickl

. Industrial Dataspace for smart manufacturing: connotation, key technologies, and framework. International Journal of Production Research. 2023; 61(12): 3868-3883.

Digital Industry transformation strategy analysis and decision-based on intelligent decision technology

Abstract

Keywords

1. Introduction

3. Proposed methodology

3.2 Process discovery and process analysis

3.3 Process redesign and reengineering

3.4 Streamlining business process

3.5 Risk management and contingency planning

3.6 Skill gap analysis

3.7 Change management

3.8 Cost benefit

3.9 Process validation and process implementation

3.10 Process monitoring

3.11 Process controlling

Table 1 Simulation parameters

4.3 Data transmission rate (%)

Funding

Data availability statement

Footnotes

Conflict of interest

References

Table 1
Simulation parameters