Enhancing Building Information Modeling Effectiveness Through Coopetition and the Industrial Internet of Things

This structured methodology provides a systematic approach to assessing the role of coopetition practices in enhancing BIM effectiveness within the Portuguese ornamental stone sector. By clearly outlining the research procedure, this framework allows for replicability and offers a foundation for future studies to expand on these findings in other sectors.

4.1. Selecting Participants

The Portuguese ornamental stone sector, deeply rooted in the nation’s cultural heritage, has been a cornerstone of architectural and construction achievements worldwide since the 15th century [37]. Renowned for its high-quality stone and engineering expertise, the sector embodies generations of accumulated knowledge and craftsmanship, making it an integral part of Portugal’s identity [38]. As the global market continues to evolve, Portugal has firmly established itself as a leading producer of stone products [39], seamlessly integrating into the international construction industry. This success highlights the sector’s competitive edge despite the country’s modest geographic size [11].

With a diverse array of companies, the Portuguese ornamental stone sector offers a broad spectrum of operational environments. This diversity makes it an ideal population for testing the hypothesis that coopetition practices can positively influence manufacturers’ contributions to improve BIM’s effectiveness in the construction industry. The sector’s established global presence—exporting to 116 countries and holding a significant position in the world stone trade—underscores its relevance to the study. According to the Portuguese Stone Federation (2022), the ornamental stone sector not only ranks as the ninth-most significant player in the World International Stone Trade, but it also secures the second position globally in terms of international trade per capita [40]. The industry’s substantial turnover of EUR 1.230 million and its support of over 16,600 direct jobs further emphasize its economic importance [41].

The sector’s mix of tradition and modernity and its readiness to engage in digital transformation make it an exemplary case for testing the hypothesis. The selection of participants for the Experimental Coopetition Network is based on criteria such as technological capability, openness to collaboration, and strategic importance within the industry. This approach ensures that the study captures a representative sample of the sector, providing valuable insights into the broader applicability of coopetition practices in enhancing BIM benefits.

Recognizing these characteristics in the Portuguese ornamental stone sector, the first step involved establishing direct and informal communication channels with the managing directors of potential participant companies, a process that uncovered three significant operational challenges:

Technological Capability: The first challenge was the limited number of companies with production systems capable of real-time connectivity to meet BIM procurement requirements. Given that BIM demands high levels of precision and interoperability, only a subset of 43 companies out of the 661 in the sector were found to have the necessary digital infrastructure. After in-person evaluations of these 43 companies, 23 were pre-selected based on their ability to integrate with an Industrial IoT platform.

Logistical Complexity and Costs: The second challenge involved the logistical complexities and costs of implementing the Experimental Coopetition Network and operationalizing data collection. The experimental nature of the project, combined with budget constraints, required careful planning to ensure its viability. Given the budget limitations, the number of participating companies had to be reduced to a minimum to manage costs effectively and create a controlled environment for testing the feasibility of BIM integration. The data collection period was also limited to two months to ensure the project remained within budget while providing meaningful insights.

Data Sharing Reluctance: The third and perhaps most critical challenge was the reluctance of companies to share sensitive data, a common issue in industries where proprietary knowledge is a critical competitive asset. In the context of BIM, where the success of a project relies on transparent and accurate data exchange, this reluctance posed a significant barrier. To overcome this, a comprehensive confidentiality agreement was developed to reassure participating companies that their competitive positions would not be compromised by their involvement in the network. This agreement covered all aspects of operations, including clientele, employee information, resources, and competitive strategies.

As a result of these strategic considerations, three ornamental stone companies formally committed to participating in the study, with confidentiality agreements signed to protect their interests. To maintain the integrity of the research and safeguard the identities of the companies involved, they are referred to anonymously as “POS.1”, “POS.2”, and “POS.3”.

This Experimental Coopetition Network functioned as a laboratory for collecting data on independent variables to explore the benefits of coopetition practices, treated as dependent variables, in testing the hypothesis that coopetition practices can positively influence manufacturers’ contributions and enhance the benefits of BIM. By fostering a coopetition environment, these selected companies could improve their ability to meet BIM requirements more effectively.

With complete participant selection and confidentiality agreements in place, the study established the Experimental Coopetition Network. This network aimed to leverage advanced Industrial IoT technology, creating a collaborative environment where participating companies could integrate their operations seamlessly. The following section describes the implementation process of this network, focusing on the technological infrastructure, including Cockpit4.0+, and the steps taken to enable secure, real-time data exchange between firms to support BIM applications.

4.2. Implementation of the Experimental Coopetition Network

The advent of the IoT marks a transformative era, significantly impacting industries through advanced sensor technologies that enhance connectivity and data exchange [42]. Building on traditional IoT frameworks, the Industrial IoT introduces intelligence into industrial environments, enabling direct device-to-device communication and creating intelligent systems that dynamically adapt to user interactions, enhancing value co-creation [43]. Empirical studies highlight the transformative potential of Industrial IoT-based innovations, particularly in offering new services such as remote control and predictive maintenance [44]. These capabilities improve operational efficiency and open new avenues for value co-creation within coopetition frameworks, expanding service portfolios and enhancing enterprises’ competitive and cooperative capacities [45].

In the Portuguese ornamental stone sector, for instance, an illustrative example of the Industrial IoT is the development of Cockpit4.0 [46], designed to connect stone companies with the digital marketplace [47]. This artifact refines product specifications and fosters collaborative interactions among machines, providers, and customers, ultimately enhancing customization and operational efficiency [48]. The current state of Cockpit4.0 exemplifies Industrial IoT’s capacity to reshape market dynamics through direct engagement and cooperative efforts.

Although Industrial IoT development was not the primary focus of this research, Cockpit4.0, representing the state-of-the-art in the Ornamental Stone sector [48], was identified as a foundational platform for transforming operational technology within the Experimental Coopetition Network. To fully leverage its potential, Cockpit4.0 was enhanced with new functionalities, such as secure connections between competing firms, leading to an advanced version called Cockpit4.0+ for this research. This upgraded Industrial IoT system, specifically designed to connect ornamental stone companies, brought enhanced technological capabilities and fostered a collaborative industrial environment conducive to a real Experimental Coopetition Network.

A key component of Cockpit4.0+ is the integration of Open Platform Communications Unified Architecture (OPC-UA), a cross-platform, open-source standard (IEC62541) for data exchange from sensors to cloud applications, developed by the OPC Foundation [49]. OPC-UA enables secure and efficient data exchange, facilitating seamless connectivity between factory floor equipment and control systems, even across different communication protocols.

By embedding OPC-UA protocols, Cockpit4.0+ addresses gaps in connectivity, efficiency, and responsiveness, creating a network where companies can thrive through collective innovation and adaptive strategies. Integrating technological innovations, such as artificial intelligence, further promotes a collaborative industrial environment, ensuring sustainable operations and competitiveness among participating companies.

Once Cockpit4.0+ was finalized, even in its prototype form, the implementation of the Experimental Coopetition Network began. Three POSs companies were formally connected (Figure 1) into a real coopetition network. These companies could now operate in certain domains as if they were a single factory linked to BIM architects’ stations, regardless of their geographical locations.

In each POS company involved in the case study, a Cockpit4.0+ system was installed, ensuring secure connectivity among companies and facilitating potential connections to BIM stations interested in prescribing customized stone globally.

With the Experimental Coopetition Network successfully established, the study defined specific metrics and KPIs necessary for assessing the impact of coopetition practices on BIM integration. The following section outlines the KPIs selected for this purpose, each aligned with the core dimensions of BIM (4D, 5D, and 6D). These metrics provide a structured approach to measuring improvements in time management, cost efficiency, and environmental sustainability—key areas where coopetition practices are expected to enhance BIM’s effectiveness in the construction industry.

4.3. Defining Metrics and KPIs for BIM Integration

BIM’s effectiveness in construction projects heavily depends on how well each component aligns with the requirements of the various BIM dimensions. The BIM.4D dimension integrates the element of time into the digital model, significantly enhancing time synchronization, accountability, and communication throughout the project lifecycle. BIM.4D enables the real-time visualization of construction sequences, which is essential for dynamic and timely project delivery.

To meet the stringent requirements of BIM.4D, manufacturers must optimize their scheduling, coordination, and productivity processes, emphasizing timely delivery [14]. The alignment of manufacturing timelines with project schedules is critical in ensuring that the overall project adheres to its planned timeline, thereby maintaining the integrity of the BIM model.

The on-time delivery (KPI_OtD) indicator is crucial for assessing a manufacturer’s ability to consistently meet project deadlines [7]. This indicator measures the percentage of stone parts delivered within the agreed timeframe (PDoT) over the total parts produced (PD), reflecting the manufacturer’s efficiency in adhering to the scheduled timeline (Equation (1)). KPI_OtD is not just a measure of logistical performance; it directly influences the effectiveness of BIM.4D by ensuring that all components arrive on time and in sequence, as required by the project schedule.

${K P I}_{O t D} (%) = \sum_{1}^{n} (\frac{{P D o T}_{(d a i l y)}}{{P D}_{(d a i l y)}})$

(1)

Improvements in KPI_OtD are therefore critical for enhancing the overall effectiveness of BIM.4D. A higher KPI_OtD means that the manufacturer is more reliably delivering components as per the project’s timeline, which supports the accurate and timely execution of the construction plan. This alignment between delivery schedules and project timelines minimizes delays and disruptions, ensuring that the benefits of BIM.4D BIM—such as improved planning, better resource management, and enhanced project coordination—are fully realized.

The BIM.5D dimension integrates cost data with the 3D model, enabling comprehensive budget and financial management throughout the project lifecycle [24]. BIM.5D is critical for optimizing project costs by providing stakeholders with detailed financial insights directly linked to the construction model [22]. This integration allows for more accurate cost estimation, resource allocation, and ongoing cost monitoring, essential for keeping projects within budget while maintaining high-quality standards.

To fully leverage the benefits of BIM.5D, manufacturers must ensure that the components they produce meet required quality standards and do so at the lowest possible cost. Achieving this requires improvements in manufacturing efficiency, which can be accomplished by optimizing human and technological resources [16].

Drawing from lean management principles, industry efficiency is often measured through productivity indicators that relate output to labor input. For this case study, labor productivity (KPI_LP) has been selected as a critical metric to evaluate efficiency. KPI_LP measures the number of parts or tasks completed (PD) by a worker (LI) within a specified timeframe, providing a clear indication of labor efficiency (Equation (2)).

${K P I}_{L P} (%) = \sum_{1}^{n} (\frac{{P D}_{(d a i l y)}}{{L I}_{(d a i l y)}})$

(2)

Enhancing KPI_LP is directly aligned with the requirements of BIM.5D. A higher KPI_LP indicates that a manufacturer is producing more with the same or fewer resources, effectively reducing costs without compromising quality. This increased efficiency supports the goals of BIM.5D by lowering overall construction costs while maintaining the project’s financial health.

By improving labor productivity, stone companies can contribute to more efficient cost management throughout the project lifecycle. This alignment between labor productivity and cost efficiency is crucial for maximizing the benefits of BIM.5D, ensuring that projects are completed within budget and provide better value for stakeholders. The focus on optimizing labor inputs while maintaining output quality directly impacts the project’s financial performance, making KPI_LP a vital metric for evaluating the success of BIM.5D in BIM-enabled construction projects.

The BIM.6D emphasizes sustainability by integrating environmental data into the BIM model, with the primary goal of reducing the ecological footprint of construction projects [15]. BIM.6D requires manufacturers to optimize the use of raw materials and processes to minimize the environmental impact of the building materials they produce [50]. This focus on sustainability is crucial in global efforts to combat climate change and promote environmentally responsible construction practices.

To evaluate how healthy fabricators are meeting the sustainability goals of BIM.6D, the CO₂ equivalent (CO₂-eq) factor is used as a critical metric [51]. This factor converts the energy consumed during production into equivalent carbon dioxide emissions, providing a standardized measure of the environmental impact associated with the manufacturing process.

Given the interconnected nature of power networks within the European Union, calculating the CO₂ equivalent for individual countries can be complex. For example, in Portugal, energy demands during critical periods often necessitate importing electricity from other European countries. These imports can significantly influence the carbon intensity of the energy used in production. Therefore, the average European CO₂ equivalent factor is applied for this case study to ensure a more accurate and standardized environmental impact assessment [52].

According to the European Electricity Review (2023), the carbon intensity of electricity across Europe varies significantly due to the diverse energy mixes of EU member states [53]. For instance, countries like Sweden and France have much lower carbon intensities (below 50 g CO₂/kWh) because of their heavy reliance on nuclear and renewable energy sources. In contrast, countries like Poland and Estonia have much higher carbon intensities (over 600 g CO₂/kWh) due to their dependence on coal and other fossil fuels. As of 2022, the EU’s average carbon intensity of electricity generation was 276 g of CO₂ per kilowatt-hour (g CO₂/kWh) [53].

Equation (3) can be employed to calculate the CO₂ equivalent (KPI_CO₂-eq) for producing parts, reflecting the energy consumed (EC) per part delivered (PD). This metric allows for a precise evaluation of the environmental impact of manufacturing processes, enabling fabricators to assess their alignment with the sustainability objectives of BIM.6D.

By reducing the KPI_CO₂-eq, manufacturers can directly contribute to the environmental goals of BIM.6D, ensuring that their operations are efficient and environmentally responsible. This alignment with BIM.6D requirements is essential for promoting sustainable practices within the construction industry and achieving broader environmental targets, such as those outlined in the Sustainable Development Goals (SDGs).

KPI_CO₂-eq

${K P I}_{C O_{2} - e q} ({KgCO}_{2} / part) = \sum_{1}^{n} (0.276 \times \frac{{E C}_{(d a i l y)}}{{P D}_{(d a i l y)}})$

(3)

Reductions in KPI_CO₂-eq directly contribute to lowering the construction industry’s CO₂ emissions and support SDG 11—Sustainable Cities and Communities—by promoting environmentally responsible practices and reducing urban carbon footprints.

With the KPIs established, the study progressed to an empirical assessment of the impact of coopetition practices on these metrics. The following section details the comparative analysis conducted over two distinct 54-day intervals, capturing baseline practices versus coopetition practices. This approach allows for an in-depth evaluation of how coopetition influences time efficiency, cost-effectiveness, and environmental impact, thereby providing valuable insights into the effectiveness of BIM dimensions within the Experimental Coopetition Network.

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Agostinho da Silva www.mdpi.com

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Enhancing Building Information Modeling Effectiveness Through Coopetition and the Industrial Internet of Things

4.1. Selecting Participants

4.2. Implementation of the Experimental Coopetition Network

4.3. Defining Metrics and KPIs for BIM Integration

Greenberg

4.1. Selecting Participants

4.2. Implementation of the Experimental Coopetition Network

4.3. Defining Metrics and KPIs for BIM Integration

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