1 Introduction

In the era of information and technology, organisations need to streamline their operations towards more responsiveness as compared to solely relying on reactive approaches. This shift in crafting business operations from manufacturing to service delivery necessitates continuously collecting real-time data from different sources such as manual operations, machines, devices and sensors (Kan et al., 2018; Peffers et al., 2007). Therefore, digital transformation can help organisations develop their strength in actionable intelligence (Baskerville & Myers, 2004; Svahn et al., 2017). For digital transformation in any business, it is necessary to identify the gaps and opportunities to conceptualise, design, develop, and implement the information systems (IS) architecture (Corbett, 2013; Kannisto et al., 2022). Well-conceptualised and designed information systems allow organisations to impact the effectiveness of data collection on a real-time basis and its processing for quick and adequate decision-making (Huang et al., 2014; Miranda et al., 2015).

An IS architecture employs evolving technologies at different nodes to transform the diverse activities of a business (Li et al., 2022; Oberländer et al., 2018). The application of these evolving technologies strengthens information processing capabilities, enabling organisations to adapt to market changes and meet internal operational requirements, reflecting the vision of an organisation (Huang et al., 2014; Margherita & Braccini, 2020; March & Scudder, 2019). Vision describes the purpose of an organisation’s existence recognised at the lowest level in designing and executing IS architecture. Therefore, Organizing Vision Theory (OVT) supports IS structure and processes in achieving transformation and innovation. From conceptualisation and designing to implementing IS architecture, numerous stakeholders with varied interests contribute to the organising vision central to the decisions and actions in an organisational set-up (Baskerville & Myers, 2004). With the rising uncertainty of the business environment, companies’ information processing capabilities, driven by organisational information processing theory (Gattiker & Goodhue, 2004), are critical for precise and quick decision-making. Corbett (2013, p.5) mentions, “managing the task environment, creating slack resources and buffers, or redefining tasks to be simpler and less inter-dependent is possible through information systems”. To address complexity and information processing requirements, organisations can either set up buffers to reduce the impact of uncertainty or structural mechanisms to enhance information flow (Seidel et al., 2013). Some organisations use information technology (IT) to advance their information flow for strategic advantage over others (Henderson & Venkatraman, 1999; Ngai et al., 2011; Peppard et al., 2000), and this further helps companies to adapt to changing market conditions.

Technology implementation case studies are rife with reports of failure due to the lack of a systems approach across sectors, which need to consider the role of technology adequately (Dahlbom & Mathiassen, 1993). Jaskó et al. (2020) advocated the influence of industry 4.0 technologies in IS to ensure seamless, secure, and trustworthy operations (Huber et al., 2022; Zhang et al., 2021; Margherita & Braccini, 2020). Henderson and Venkatraman (1999) advocated the role of IT as a strategic element. Another study underlined the role of information technology in transforming organisations, such as reducing costs through data analytics, leading to improved efficiency of business operations (Ngai et al., 2011). Most of the studies discussed the role of IT systems (Miranda et al., 2015; Ramiller & Swanson, 2003) while considering the vision and information processing requirements while ignoring the integration of IT into operational technology (OT) systems. Furthermore, the literature highlights that companies use either software or technologies to extract information in a business (Naedele et al., 2015; Wollschlaeger et al., 2017) but lack a structured and grounded theory approach in the development of architecture (structure) and implementation (process). Currently, organisations encounter challenges in capturing, processing, analysing data and offering decision support, often lacking a coherent and structured predictive system. With these gaps in structure and process in mind, this study developed architecture and employed an IIoT-driven information system to enable quick and accurate decision-making. Therefore, this study aims to:

Provide a one-stop solution for all plant data capturing, cleansing, storage, analysis, and decision support.

Develop an IIoT-driven information system to ensure the flow of information across departments. The contribution of this study is that it creates knowledge by embedding OVT- and OIPT-based IS architecture with AI and ML orientation for accurate and quick decision-making, including maintenance activities. The study’s contribution lies in developing (i) a self-organising information system, (ii) a transparent and visible information system, (iii) mapping the OVT and OIPT elements to advance phases of conceptualisation, development and execution, (iv) highlighting the new knowledge development through IIoT based IS to facilitate better decision making in aspects like data collection, data integration, reporting, analytics and accessibility for better decision making. The remainder of this paper is organised as follows: In the next section, the motivation for conducting the study is presented. Section 3 indicates the underpinning elements. Section 4 highlights the organisation and place of the case study. Section 5 presents the research design, and Sect. 6 presents the results. In Sect. 7, findings are discussed, followed by lessons learned at Tata Metaliks Limited (TML) in Sect. 8. To this end, the scope for future research is presented in Sect. 9.

2 Motivation to Conduct the Study

Presently, the information processing capabilities of an organization significantly shape its business landscape. Nonetheless, numerous processes lack a self-organizing mechanism, which is pivotal in the realm of the digital world and digital processes in business operations (Berger et al., 2021; Chiasson & Davidson, 2005). The vision of TML is “Reaching Tomorrow First”, emphasizing the importance of preparing today to address the challenges of tomorrow. With this in mind, architecture-driven Organizing Vision Theory (OVT) and Organizational Information Processing Theory (OIPT), which enhance the competitiveness of an organization, are suitable for digitally transforming an organizational system (Chou & Shao, 2022; Han et al., 2021; Gattiker & Goodhue, 2004; Ramiller & Swanson, 2003). A vision centred on digital architecture exerts a profound influence on vital domains, from procurement to production optimization to advanced analytics in maintenance activities. This, in turn, enables enhanced data governance and the formulation of innovative strategic approaches (Berger et al., 2021; Sicari et al., 2016).

It is observed that TML’s infrastructure helps balance cost and flexibility (Zhong et al., 2013) in a limited way, which impacts value for critical stakeholders. Further, it is noted that a significant gap exists between data demand and supply due to a traditional IS with a poorly designed data supply network. This deficiency results in a non-systematic approach to creating value. Network data flows continuously between customers, suppliers, enterprise IT systems, connected devices, and equipment (Zhang et al., 2017). TML’s network comprises programmable logic controllers (PLCs)/supervisory control and data acquisition (SCADA) systems, IIoT-based sensors, data generated from labs, enterprise resource planning (ERP) systems, and logbooks. These assets generate different data types used in decision-making at different stages of business operations. The problem of factual, data-driven decision-making capable of handling large and different data types is observed at TML. On the one hand, TML is committed to adopting Industry 4.0-oriented technologies from the shop floor to the corporate office. However, on the other hand, initially, TML struggled to conceptualize how to transform every aspect of connectivity, processing, real-time analytics, and decision-making assistance with the appropriate structures and processes.

With the open market of available Industry 4.0 technologies, it is difficult for TML to balance the various dimensions of the business to achieve the desired value creation for better decision-making aligned with the organizational business goals and vision. Balancing robustness and resilience in the architecture and implementation process can enable digital transformation, such as tracking the stages of the ductile and pig iron pipe production process and specific fail-safe measures. Therefore, to meet internal and external customer demands accurately, make quick decisions, and drive business results more effectively, TML started this architectural transformation in their business units in 2019, with phased implementation in other departments and business units.

3 Underpinning Elements

This section outlines existing knowledge and gaps in IS architecture, IIoT, OVT, and OIP-grounded theories.

3.1 IS Architecture

Across sectors, digital transformation is underway through cyber-physical systems that facilitate a decentralized vision and self-optimization and ensure better control of processes and products (Oks et al., 2022; Zhang et al., 2021). At TML, information processing becomes even more critical due to the volume, velocity, and variety of extensive data flowing through different stakeholders (Huang et al., 2014; Premkumar et al., 2005). The IS architecture integrates processes, physical resources, IT systems, and information from the network to present a value-oriented framework (Berger et al., 2021; Kannisto et al., 2020); however, the role of each needs to be clarified. The IS architecture mobilizes fundamentals to enable mediation and functionality but needs to integrate demand and supply data (Wiederhold, 1992). The adoption of IIoT as a part of an operations technology (OT) system in supporting system servers where the data structure is unclear (ur Rehman et al., 2019; Mohammadi et al., 2018). Predictive analytics offers the simulation and statistical parameters of the process to facilitate the status of each machine and workstation, along with the units to be produced (March & Scudder, 2019). The integrated IS architecture adds value to the workforce and the machines at the operational level.

Furthermore, integrated IS architecture can present a dashboard of activities to facilitate quick decision-making (Fang et al., 2015), requiring immediate customization up to mobile applications. Existing literature on IS architecture considers data facilitation in the form of ERP systems (Gattiker & Goodhue, 2004) and focus either on traditional IS (Wollschlaeger et al., 2017) or leveraging IT for new product development (Pavlou & El Sawy, 2006; Sicari et al., 2016); these have limited space for transformation and value creation. What needs to be added is the harmonization of machines, devices, gateways, human-machine coordination and cloud networks to employ AI and ML for predictive analytics and quick decision-making.

3.2 Industrial Internet of Things

Technology is an integral part of enabling operations in most companies. IIoT harnesses the capabilities of machine learning and artificial intelligence technologies to enable faster automation and machine-to-machine communication (Mahdavinejad et al., 2018). A typical business environment needs to influence and integrate existing architecture and infrastructure, and IIoT offers a space for doing so (Sun et al., 2017; Fang et al., 2015). IIoT facilitates not only automation but also robust design of ISs that can enable analytics and connectivity with smart devices. In existing studies, IIoT is considered for parallel computing for quick machine-related information processing (Kan et al., 2018), not including File Transfer Protocol (FTP) or Open Platform Communications (OPC) elements rooted in the PLCs/SCADA of an OT system. IIoT platforms, along with others such as Fast Fourier Transform (FFT) servers and system servers, enable data integration and are considered in the existing studies (Puschmann & Alt, 2005), where a knowledge gap exists. Adopting IIoT into business processes can shift a traditional shop floor system to a distributed platform. Hence, IIoT offers an integrative perspective for the shop floor, reinforcing sound business and operational decisions.

3.3 Organizing Vision and Organizational Information Processing Theory

The flow of information, materials and funds is vital in organisations. Out of these three types of flow, information is critical as it facilitates the effective execution of tasks (Chou & Shao, 2022; Galbraith, 1973; Huang et al., 2014). Organising vision theory offers a structured framework for understanding how organisations operate, evolve and adapt in a dynamic and complex environment. The theory supports decision-making aligning with company goals, values, and structure. This study considered OVT for developing an architecture that can understand, analyse and improve organisational processes in a complex, dynamic and continuously evolving world.

On the other hand, organisational information processing theory offers a framework to design the structure and processes by aligning strategic goals. The theory also highlights that there is no one-size-fits-all approach, and hence, organisations should design their systems keeping their requirement in mind. Hence, these two theories were found suitable for the study. Firms can choose between two survival options: either reduce their use of information or enhance their information processing capabilities (Huber et al., 2022). The former seems impossible in the age of data, uncertainty and information, and the trend over the last two decades favours the latter. Multidirectional, multichannel and multidimensional flow of data into the business from functional teams and external stakeholders challenge firms to hone their information processing capabilities (Premkumar et al., 2005). Information systems must acquire valuable information from both upstream and downstream in a supply chain to enhance information visibility and facilitate quick decision-making (Ngai et al., 2011). Existing literature indicates that information processing through digital architecture involves a structure for organised knowledge exemplified in different elements (Han et al., 2021; Leng et al., 2020). Some of the studies consider IS architecture merely as a means for structuring dispersed information without considering the business’s objectives and vision (Jaskó et al., 2020); others fail to consider the adequate degree of information processing capabilities (Gattiker & Goodhue, 2004; Huber et al., 2022). This study addresses this gap, mobilising organisational vision theory to design and develop adequate information processing capabilities through an IIoT-driven IS based on a robust architecture to develop a new knowledge system for an organisation.

4 About the Organization

TML (a subsidiary of Tata Steel) produces ductile iron (DI) and pig iron (PI) pipes in India (Kharagpur, West Bengal) and was incorporated in 1990. TML has 300,000 tonnes installed capacity for PI and 250,000 tonnes for DI pipes. The company turnover for the year 2019–2020 is USD$277 million. The company is committed to serving its customers worldwide with tailored solutions and products. TML is highly customer-centric, and therefore, technological efficiency at the plant remains one of the priority and competitive constituents that further facilitate accurate and quick decision-making. In 2019, the organisation planned its digitalisation strategy aligned with its vision of “Reaching tomorrow first”, staying ahead of the competition and capitalising on trends of the future to fuel the growth of the organisation. This also indicates that TML is focused on innovation and adaptability and anticipating customer needs. The organisation’s mission is to contribute to water and sanitary sectors with the optimal utilisation of resources, materials and energy. The vision and mission are guided by integrity, responsibility, excellence, pioneering and unity. To facilitate better and quick decision-making, TML has identified the DI pipes department to start with and develop an information system that collects, processes, analyses, and disseminates the data to different stakeholders to make informed decisions. Through this system, the stakeholders can offer reliable, timely and actionable information to facilitate quick and accurate decision-making in day-to-day operations. Hence, the organisation has undertaken the clinical research project to develop an IIoT-driven information system which is self-organising and facilitates better decision-making.

5 Research Design

The study employs a systematic methodology to conduct this clinical research. It comprises four stages, representing the alignment of business objectives, data governance, and data strategy to achieve a self-organizing vision in the form of quick and reliable decision support, as highlighted in Fig. 1.

Fig. 1
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Conceptual framework for a self-organizing vision and IIoT driven information system

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5.1 Case I (DI Pipes)

To develop an IIoT-driven IS for the DI pipes plant, the conceptual framework presented in Fig. 1, a four-layer mechanism, is developed (see Fig. 2), representing the self-organizing vision of an IIoT-driven information system. Finally, the system architecture is developed, integrating an operational technology system into the IT system presented in Fig. 3 to achieve digital transformation in the first phase in the DI pipe plant. Further study identified and developed a pioneering knowledge system based on TML’s customer centricity, an integral part of the company. This information system is based on the OVT theory coined by Swanson and Ramiller (1997), along with enhancing information-processing capabilities (Huber et al., 2022) through the application of the OIPT theory (Galbraith, 1973). This way, organizations can use digital technologies to design their infrastructure and architecture to drive value and maximize impact to address the competitive landscape. This research design is presented in three critical phases – conceptualization, development, and execution of an IIoT-driven information system further mapped to OVT and OIPT theory elements that facilitate self-organizing vision as a knowledge contribution, indicated in Fig. 4.

Fig. 2
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Solution for self-organizing and IIoT driven information system

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Fig. 3
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Implementation of the OT and IT system architecture among IIoT based information system

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Fig. 4
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Mapping of TML information system architecture to OVT and OIPT

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A four-step mechanism is conceptualized to integrate the OT and IT systems for unique decision support through IIoT-driven information systems. At first, the existing PLCs/SCADA system is upgraded, which acts as an input to the existing system for storing historical data. This creates an information layer further, collecting data from diverse sources, including extracting data from OT, to develop an IT data integration network. This network facilitates an overview of the plant, dashboards, and reports originating from the different departments, in addition to the information circulating throughout the departments and stored in the cloud network. A cloud network leads to knowledge layer development in the form of AI/ML-based decision support, including predictive analytics of maintenance operations that are self-organizing and automatically developed based on machine and equipment conditions (Lee et al., 2020).

Our solution involves integrating the operations of the OT and IT, wherein the OT systems and Open Platform Communication (OPC) servers capture the data from the Centrifugal Casting Machine (CCM), Finishing Lines (1,2, and 3), and Annealing Furnace SCADA system. Additionally, the FTP server extracts the data from laboratory computers and the IIoT ignition server from the IIoT sensors. Collectively, the OPC, FTP, and IIoT ignition servers integrate, transfer and store data to system platform servers and that further transfer it to the system application and database server. This data further flows through a firewall to the IT system, where it simultaneously interacts with the ERP and departmental dashboards and facilitates the mobile applications, bulk messages to the workforce, and the AI/ML-based decision support system (Lee et al., 2020; Liu et al., 2017; Sun et al., 2017). In this way, AI/ML-based information systems are flexible enough to capture the information that helps the architecture further self-organize.

Data flows through four stages, from data sourcing to its storage and management to data consumption and how it leads to value creation and facilitates quick, reliable and actionable decision-making. Hence, the data demand and supply work on a pull system that defines the data governance and data strategy for inclusive information flow that reaches AI and ML engines for further analysis on condition-based monitoring (Kan et al., 2018). Sensor-generated data is collected and analysed on heat level, number of units produced, operational qualification and warning signs for maintenance. With an IIOT-driven information system, it is possible to track the real-time condition of a machine as compared to its average lifespan, identify the performance and schedule maintenance to ensure uninterrupted operations. IIoT-driven IS also enable the organisation’s growth by reducing maintenance cost and helping in optimising the capital investment among machines and equipment.

Figure 3 indicates the integration of IT and OT systems, where ERP systems such as SAP in IT and manufacturing execution system (MES) are integrated with OPC, FTP and IIoT ignition server, which results in RESTful application program interface (API) to exchange the information securely over the internet and the seeded mobile phone also receive the SMS alert for a particular machine as well as on the PC of the department.

5.2 Case II (Pig Iron)

After developing the IIoT-based information system in the DI pipe plant, TML is implementing the same in the Pig Iron plant to improve the bottom line of business operations, where IIoT-based information systems can help contribute to enhancing reliable, actionable and accurate information to facilitate decision-making. In this department, the IIoT-driven information system is successfully tested by TML for the blast furnace in the PI plant. In 2022-23, after implementing it for DI pipes, TML tested the IIoT-driven information system integrating OT and IT systems, where the system is tested for its ability to cluster different operating modes of the blast furnace. The architecture and system are tested for adaptability to identify and predict near future failures and track the health status of equipment in the Pig Iron department. This initial testing of IIoT-driven IS in the Pig Iron department offers automation, reliable and quick decision-making harnessing AI and ML technologies, contributing further to aligning operations and enhancing the profitability and growth of TML. Figure 5 designates critical IT and OT systems that enable the smooth operations of a blast furnace. The process of maintaining the blast temperature, volume, pressure, top gas pressure, steam injection rate, etc., is maintained by the process control system.

Fig. 5
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IIoT based information system integrating OT and IT systems for blast furnace

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In contrast, the lab system is connected to the server through LAN (Local Area Network). OPC workstation works with OT systems and operates on PLC to produce the Pig Iron through a structured process. The OPC workstation, through PLCs, helps monitor, communicate, control, and optimize the operations of a blast furnace. OPC workstation gathers data from multiple sensors and equipment, along with other devices of the blast furnace system. OPC workstations enable the graphical representation, including charts and dashboards, facilitating better decision-making through real-time monitoring. The IIoT-based information system in the blast furnace department of the PI plant helps monitor irregular conditions and variations from the operating specification and triggers an alarm when required. This way, operators are alerted to any potential issue to avoid disruption and hazards while working in the blast furnace plant. Further, the OPC workstation is integrated with the SCADA system, which provides a detailed view of the entire process. The IIoT-based system in the PI plant allows operators to make adjustments to process air parameters, fuel supply feed rate, Etc. PLC stock house is designed to offer high operating flexibility for batch composition and charging sequence and minimize the transfer points; the PLS cast house helps to maintain the different equipment used to handle the metal when producing pig iron. These OT systems help receive and send the data to IT systems and monitor the information through IoT sensors for data being stored in a central system.

6 Results

The main objective of this study is to develop a new knowledge system at TML to facilitate better decision-making in its DI and PI pipe plants, increase productivity, and share competitiveness with other business units. This study contains key themes of TML’s digital transformation journey, i.e., real-time data flow, analytics, and mobility for quick and accurate decision-making employing emerging technologies. A study designed and developed IIoT-driven IS, which is core to Industry 4.0 at TML (Huber et al., 2022; Li et al., 2022; Oberländer et al., 2018; Zhang et al., 2021).

One of the significant challenges in creating value from the data is the availability of data and its structure (both real-time and historical). Further, whether data is in a central location or can be extracted to develop descriptive, predictive, prescriptive, and cognitive models at different stages of business activities is the critical concern. This is even more challenging in a legacy business like a manufacturing plant, where multiple data collection engines work separately and on different platforms and reports are generated manually (see Fig. 1: Data Sourcing). For example, at TML, all machine-related data are primarily available at the PLC level. These data are scattered and collected locally, whereas plant operations-related data is collected in a physical logbook or, at maximum, in Excel sheets on computers. Earlier, dispatch-related data was collected through an ERP system (SAP S4 Hana). The architecture and implementation of IIoT-driven IS offered a platform where different data sources connected seamlessly through an OPC, FTP, and IIoT ignition system. The OT and IT system integration presented a robust information system architecture (Huang et al., 2014; Puschmann & Alt, 2005) with the latest, upgraded hardware, software, and cyber security measures (Oks et al., 2022). The data generated through OT systems facilitates a decision-support system through AI and ML for ultimate value creation (Liu et al., 2017).

Comparing Case I and Case II, the DI Pipes production unit has processes that are scattered in different departments as compared to Pig Iron (Blast Furnace). Further, DI pipes have a separate IIoT ignition server, whereas, in the case of the blast furnace of the PI plant, there is an IIoT central server, which is coupled to the OPC workstation, offering quick, reliable and actionable decision support. However, both the ISs are developed on the same principle of how IIoT-based IS can best integrate IT and OT systems. For example, an OPC server is required in both cases, where the server translates the hardware communication through PLC into OPC protocol, and it acts as a standard interface among different types of data sources ranging from lab testing equipment to databases, facilitating superior decision-making.

7 Discussion

A robust IS architecture and its stage-wise implementation define the success of the digital transformation, improving information processing capabilities, indicating how the vision of organizing processes changes according to the dynamic requirements of departments, customers and the market at large (Huang et al., 2014; Pavlou & El Sawy, 2006). This study employs a structured methodology for executing the IIoT-driven IS, where integrated OT and IT systems are integrated, confirming the organizational vision (in consistency and endurance) of the processes. It further demonstrates the features of efficient information processing capabilities (quality of information/data, ability to address the uncertainty of the environment, i.e., the resilience of IS) (Huang et al., 2014; Liu et al., 2017; Urquhart et al., 2010). A vital contribution of the study, therefore, is the mobilizing of OVT and OIPT to develop and implement an integrative IIoT-driven IS architecture in a dynamic customer-centric environment with rapidly changing customization requirements to support quick decision-making. The implications of this work for professionals, researchers, and policymakers are presented as follows.

7.1 Managerial Implications

Existing studies advocate the importance of IT in developing ISs and their architecture. The findings of this study broaden the existing view of integrating OT and IT as a means of enhancing an information-processing infrastructure for internal customers and enabling the integration of external stakeholder requirements. Professionals from ISs can simplify the designing of IS architecture, focusing on transforming existing dispersed systems to become self-organising and able to influence precise decision-making based on AI and ML. Other organisations can use the concept of pulling OT-generated data through IT systems. Thus, system platform servers and historians support a self-organising vision and innovative IS employing AI and ML along with IIoT, which can facilitate not only data capture and storage but also fetch accurate data according to the problem at hand and predict near-to-precise maintenance activities for machines and equipment. While dealing with ground-breaking technologies such as AI and ML, the executives should also consider the cyber security of the system. Executives also need to understand the requirements of each department, whether the same technologies, both from IT and OT perspectives, can work, whether the same type of IS architecture can work, or whether we need something else. It can be noted that in this study, Case I (DI Pipes) required more systems due to the complexity of the process and the number of stakeholders involved. In contrast, comparatively, in Case II (Pig Iron), the process is more straightforward, working on the same principle.

For business partners, the study’s findings indicate that different business nodes and mapping can be identified for each element of the vision and information processing capability to enhance the overall organisational performance by developing a new knowledge system. IS developed in this case study can act as a guide for planning systematic implementation while harnessing the features of IIoT, AI, and ML technologies (Oberländer et al., 2018). In short, this study offers experts, developers, executives, and top management a systematic approach to achieving digital transformation based on OVT and OIPT (Miranda et al., 2015; Swanson & Ramiller, 1997; Galbraith, 1973).

The study tried to answer the central questions, such as how to design and develop an IS architecture to capture, cleanse, store, analyse and make a quick and data-driven decision. Further, the study develops an AI and ML-based decision support system to predict accurate and timely maintenance of machines and equipment in case I and new knowledge systems. In contrast, case II focuses on integrating IT and OT systems. These questions are critical for professionals involved in integrating typical manufacturing systems, which are usually labour-intensive, and where information technologies can help achieve enhanced performance of business operations. This can further help organisations improve productivity not only in terms of production but also in terms of effective asset and resource utilisation by integrating operational and information technology.

Additionally, the study unveils the execution of a framework for self-organising vision and IIoT-driven IS that can help managers have an integrative view of business goals, vision and key performance indicators. Further, Table 1 explains how IIoT-based IS helps develop new knowledge and actionable insights to facilitate better decision-making compared to the old knowledge system at TML. Table 1 also indicate the measurement system and impact observed under new system. The comparison between old and new knowledge systems highlights the data collection, data integration, reporting, analytics, and accessibility features facilitating better decision-making at TML.

Table 1 Comparison of old and new knowledge system for better decision making at TML
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7.2 Theoretical Contributions

Collectively, the findings of this research validate the organizing vision theory argument, holding that “through the actions of customers and society, there are changes in the organization’s vision, and this can reciprocally influence needs and attract new customers to the business” (Swanson & Ramiller, 1997). This study enriches and extends OVT and OIPT (Miranda et al., 2015; Swanson & Ramiller, 1997; Galbraith, 1973) in three ways. First, it addresses the research objective of self-organizing that aligns the vision of enabling the storage and cleaning of data for decision-making through different servers to an IIoT-driven IS. This self-organizing IIoT-based IS enable monitoring, controlling and optimizing different business processes. The information and data generated by a self-organizing vision-based approach further enhance professionals’ understanding of interconnected elements that can pave the way for continuous improvement (Lenz et al., 2020). The proposed IIoT-driven IS improves the information-processing capabilities regarding visibility and transparency across layers (from top management to operator level) (Jaskó et al., 2020). Second, by addressing the research objective of tracking the exact location of concern, production count, defect rate, and process parameters both in Case I and II at a particular stage so that they match societal and customer expectations through information-processing and technological innovations (Svahn et al., 2017).

Capable and self-organizing IS can further contribute to new product development and designing the supply chain and logistics activities for an enterprise (Jaskó et al., 2020). The employment of industry 4.0-oriented technologies influences the functionalities of an IS. Third, it addresses the research objective of developing an AI/ML-based system that can accurately predict the maintenance of machines and allocation of machines to ensure desired productivity and efficiency (Huang et al., 2014). This study contributes to interconnecting cyber-physical systems of an IIoT-based IS to offer unified, secure, accurate and quick information sharing in shop-floor operations (Oks et al., 2022). The developed IS also offers inter-operability that captures data from diverse sources that further feed to IT systems to facilitate quick and accurate decision support. The IT system processes the information into meaningful charts, graphs and signs that facilitate day-to-day operations from bottom to top management and vice-versa. In this matter, technology is employed to augment the system’s information processing capabilities.

Consequently, integrating OT and IT is essential in creating digital tools, including a dashboard to monitor the processes regularly. These tools, in turn, enable remote management and data-driven decision-making in both Case I and Case II. In summary, this study presents a case of the conceptualization, design, development, and implementation of self-organizing and efficient IS at TML’s DI Pipes and Pig Iron (Blast Furnace) department that needs to be benchmarked by other organizations while considering their supporting OT infrastructure.

7.3 Policy Implications

This study at TML offers policy-level implications for other organizations considering conceptualization and implementation or are currently implementing or have already implemented any IS architecture seeking seamless information flow and effective and factual decision-making. However, this does not imply that the same approach may work for other organizations. Successful adoption of societal expectations in the vision and alignment of information processing capabilities to fulfil customer preferences through quick decision-making will encourage other firms to invest in soft infrastructure and build future organizations, creating data-driven value that can act as new knowledge. The timely conceptualization and implementation of robust system architecture based on organizational vision can pave the way for a long-term plan in a complex and continuously changing business environment (Kannisto et al., 2020; Seidel et al., 2013; Scheepers & Scheepers, 2008). This will support further positive changes for new knowledge systems that can address other concerns of superior decision-making in business operations (Mathiassen, 2002). To present the study’s contribution precisely and objectively, it mapped the elements of OVT and OIPT to the conceptualization, development, and execution, as presented in Fig. 4.

The mapping of architecture to organisational theories facilitates quick decision support through a self-organising vision of IIoT-driven IS (Kannisto et al., 2022). The self-organising vision supports quick and accurate decision-making through transparency, continuity, coherence, addressing information needs, and enhancing the system’s capabilities. The mapping of IIoT-driven IS to the theoretical lens is critical to the clarity of these elements and underscores their crucial role in the stages of conceptualisation, development, and implementation that are self-organising and continuous. The mapping helps monitor new knowledge, or new decision support and information flow mechanisms, that can refine elements of organisational vision that were achieved previously at a relatively lower degree.

IIoT-driven IS at TML’s DI Pipes and Pig Iron (Blast Furnace) department extracts and sources the data from different sources to create value for quick and accurate decision-making, where different types of OT servers feed into IT. The study further expands OVT and OIPT literature by mapping the stages of conceptualisation, development and execution corresponding to different characteristics of the self-organising vision of TML IIoT-driven IS architecture. The existing constraints and infrastructure, along with information flow, are considered. The existing plant structure and information technology platforms aid in integrating the data and validating the reports to develop a predictive and secure platform. In the last phase, the operational and information technology elements integrate through dashboards, alarms and applications. This ends in developing and executing a File Transfer Protocol (FTP), Open Platform Communications (OPC) and IIoT-enabled architecture to drive value in quick, accurate and factual decision-making.

8 Key Lessons Learned

TML is committed to designing, developing and producing excellent products; this study focuses not only on the operations and resources but also on involving upstream and downstream partners to organize the vision and enhance the information processing capabilities for quick, accurate and factual decision-making. With the conceptualization, development, and execution of IIoT-driven IS, this study addresses three elements of OVT and two elements of OIPT.

8.1 Transparency of Vision

Transparency in sharing and organizing with essential stakeholders, such as employees, towards a shared vision builds trust, along with their involvement in refining processes. The processes, with their constraints, lead to identifying various platforms of data sourcing that ultimately end in integrating OT and IT systems while executing a flexible system architecture based on IIoT. Therefore, organizations conceptualizing and changing their data generation points must also align their existing constraints within the IS before integrating their OT and IT systems for more transparency.

8.2 Continuity of Organizational Vision

With the increasing complexity of the business environment and pressure to warrant business continuity, even uncertainty demands continuous data and information support. Hence, TML conceptualized the appropriate data storage to facilitate quick and more accurate day-to-day business decisions. While executing the system architecture, TML witnessed the usage of dashboards and reports along with data-driven alarms to facilitate the continuity of complex and intricate decisions. Other organizations can develop and implement IIoT-based IS elements to facilitate innovative and continuous changes in quick and factual decision-making that are data-driven and integrated.

8.3 Vision Coherence

The business processes must be coherent for effective alignment with the company’s mission, vision, and values before any system-oriented innovation. The degree of coherence will define how the organization achieves its vision, and that starts with how stakeholders of a business consume data for different decision-making scenarios that facilitate the idea of developing an innovative and integrative IS platform. Therefore, the online tracking and availability of the same applications across the organization create quantifiable measures reinforcing coherence. The organizations and professionals involved in developing and using this IS need to be in synchronization to understand the information flow in their company, i.e., how many nodes are present and how these can align with existing infrastructure, aligning vision and degree of information processing.

8.4 Information Processing Needs

TML’s key focus is on the design, development and production of DI pipes. To conceptualize and implement an IoT-driven IS at TML, the communication required from the laboratory, IIoT sensors, PLCs/SCADA, and logbooks of the manufacturing and logistics processes are identified. TML identified the role that integrative information can play in validating the types of data and reports generated at different locations and departments in the plant. On this basis, TML developed and executed an analysis and decision support system, keeping the information processing required in mind. The practising managers in the IS domain must view the fit from a strategic perspective. Information processing needs can utilize mobility applications, where the status of business activity at different stages can be tracked, including remotely, for better decision-making.

8.5 Information Processing Capabilities

Information processing capabilities define the level of system architecture required to address the information needs. Initially, we conceptualized upgrading the regular ERP with the help of SAP (ERP vendor). However, regarding inter-organizational and inter-functional interactions, TML decided to include a range of technologies, such as IIoT, OPC, FTP, AI/ML, along with multiple types of servers. Each organization have different information-processing requirements according to its functional areas and needs integration towards seamless information flow to facilitate decision-making. At TML, this difference in information-processing requirements is evident from Case I and Case II. The information-processing capabilities are to be developed based on the frequency of necessary communication, arrangement of the OT and IT systems, and integration of needs with other functions such as procurement and design. In addition, other organizations seeking to advance IS need to look at the fit of information processing needs and capabilities to influence the decision-making style of an organization.

9 Future Scope of Work at TML

Two units in the TML plant produce DI pipes and pig iron. Until now, TML used the DI pipe plant for this IIoT-driven information system. However, TML has tested the IIoT-driven information system in pig iron operations in the initial stage and is tested for the blast furnace department, where the principle of IIoT architecture remains the same except for some minor changes. Regarding industry 4.0 technologies, so far, TML has successfully employed an IIoT-based system that includes AI/ML and other servers integrating OT and IT systems to organize the vision of TML, along with our information processing capabilities for quick and accurate decision-making. TML is developing and refining the framework that can integrate the production of DI and pig iron pipes through a refined system architecture planned for implementation at other plants of the TATA group. Future studies may examine this process from a different theoretical lens while developing an information system architecture that supports the emergence of knowledge processes.