Analyzing the capability-attractiveness of Internet of Things technology applications in the food industry

The food industry has been transformed by the Fourth Industrial Revolution, particularly through the application of the Internet of Things (IoT). This technology enhances efficiency by connecting various components of the factory—both wired and wirelessly—and paves the way for smart factories aligned with sustainability goals. The aim of this research is to analyze the capability–attractiveness of IoT applications in the food industry based on sustainability indicators and the readiness of selected companies in the food industry of Tehran province to implement these technologies. First, a systematic literature review was conducted to identify relevant IoT applications in the food industry, along with sustainability-based attractiveness indicators and capability criteria. The case study is selected companies in the food industry of Tehran province and their subsidiaries, which are currently deploying IoT technologies across various areas. Using the Best-Worst Method (BWM), the weights of the indicators were determined. Then, decision matrices were developed separately for evaluating the applications based on attractiveness (sustainability) and capability indicators, and each application was scored accordingly. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was then used to obtain final rankings. Based on the capability–attractiveness matrix, the most promising IoT applications identified for implementation in the company include “real-time data collection,” “inventory management and shelf replenishment,” “energy consumption management,” and “smart fire detection systems.” The findings offer valuable insights for identifying and adopting IoT applications in the food industry, considering the capacities and infrastructure of companies. Introduction The food industry, a fundamental sector for human needs, faces increasing demand, customer expectations, and intense competition. To ensure food safety and profitability, companies are adopting advanced technologies like the Internet of Things. IoT, through networks of sensors and smart devices, enables intelligent interaction between equipment, machinery, and information systems, enhancing efficiency, streamlining processes, and supporting sustainable development. The Iranian food industry faces challenges such as high food waste, weak supply chain traceability, inefficient resource management, and ongoing quality concerns. IoT can effectively address these issues, yet many companies have not fully adopted it. This research provides a systematic approach to examine and prioritize IoT applications based on sustainability attractiveness and the capability of active companies in Tehran province. The main research question is: What is the implementation priority of IoT applications in active companies in Tehran province based on the attractiveness of each application and the companies' capability to acquire this technology? Research Background The Iranian food industry confronts significant challenges that threaten its sustainability and competitiveness, including extensive food waste during production, storage, and distribution, weak traceability in the supply chain, inefficient management of critical resources such as water and energy, and persistent product quality and safety risks. Lack of effective infrastructure for tracking and verifying food authenticity reduces consumer trust and enables food fraud. In this context, IoT technology emerges as a modern and efficient solution. By employing smart sensors to monitor storage and transportation, tracking systems in the supply chain, real-time monitoring of raw materials and products, and wearable devices to enhance worker safety, IoT can increase productivity, improve food safety, reduce waste, and strengthen consumer confidence. Targeted IoT adoption can address structural problems and enhance Iran’s national and international food industry standing. Despite this potential, many companies have not yet fully embraced IoT. This research seeks to provide a systematic approach to examining and prioritizing IoT applications while considering their sustainability benefits and internal company capabilities. Methodology This applied research adopts a quantitative, descriptive-survey, and cross-sectional approach, utilizing both library and field methods for data collection. The initial phase involved a systematic literature review to identify IoT applications relevant to the food industry, along with sustainability-based attractiveness indicators and capability criteria. Through this process, twelve key IoT applications were identified, such as real-time data collection, smart fire detection systems, and energy management. Additionally, nine sustainability indicators were defined across economic dimensions—including operational cost savings—social dimensions, such as customer satisfaction, and environmental dimensions, like waste reduction. Furthermore, eight capability indicators were established, covering areas such as platform development, security, and regulatory compliance. The study targeted experts from the food industry in Tehran province, all with a minimum of five years of experience in IoT-related projects. A judgmental sampling method was employed, and data were collected from seven selected experts. To determine the weights of the attractiveness and capability indicators, the Best-Worst Method (BWM) was applied. The experts completed BWM questionnaires, and the final group weights were derived by calculating the arithmetic mean of their responses. Consistency ratios were also computed to verify the reliability of the comparisons. Following this, separate decision matrices were constructed to evaluate the twelve IoT applications based on the weighted attractiveness and capability indicators. Each application was scored by the experts using a 10-point Likert scale. The TOPSIS method was subsequently employed to process these matrices, yielding final scores and rankings for the applications according to each dimension. Finally, a Capability-Attractiveness Matrix (ACM) was developed. The TOPSIS scores for attractiveness, represented on the vertical axis, and capability, on the horizontal axis, were plotted for each application. The mean scores of all applications served as cutoff points, dividing the matrix into four distinct quadrants and thereby enabling strategic prioritization of the IoT applications. Findings The BWM analysis revealed the relative importance of the indicators. For attractiveness, the economic dimension was the most critical (0.725), followed by the social (0.175) and environmental (0.100) dimensions. Among all sub-indicators, "operational cost savings" (EC1) had the highest final weight (0.494), underscoring its paramount importance. For capability, "IoT platform development" (CAP3) was the most significant indicator (0.305), followed by "application development" (CAP4) and "security capability" (CAP5). All consistency ratios were within acceptable limits, confirming the reliability of the expert judgments. The TOPSIS analysis provided separate rankings based on attractiveness and capability. Based on attractiveness (sustainability benefits), the top applications were "real-time data collection" (A1), "inventory management" (A10), and "energy consumption management" (A11). Based on capability (ease of implementation), the top applications were "smart fire detection" (A2), "real-time data collection" (A1), and "energy consumption management" (A11). The integration of the TOPSIS results into the ACM yielded the final strategic prioritization. The applications were categorized into four quadrants: Quadrant 1 (High Attractiveness, High Capability) contained the most promising applications for immediate implementation: A1 (Real-Time Data Collection), A10 (Inventory Management), A11 (Energy Management), and A2 (Smart Fire Detection). These represent the first priority. Quadrant 2 (High Attractiveness, Low Capability) included applications A5 (Operational Cost Control), A4 (Process Automation), and A8 (Remote Facility Control). They are desirable but require capability-building efforts, marking them as a second priority. Quadrant 3 (Low Attractiveness, High Capability) contained applications A6 (Quality Monitoring) and A7 (Worker Health Monitoring). While companies have the capability, the perceived sustainability benefits are lower. These could be developed after Quadrant 1 applications. Quadrant 4 (Low Attractiveness, Low Capability) included applications A12 (Supplier Tracking), A3 (Worker Tracking), and A9 (Environmental Monitoring), indicating the lowest priority for implementation. Discussion and conclusion This study identified and prioritized IoT applications for the food industry in Tehran province using a structured Capability-Attractiveness framework. The findings indicate that the primary focus for companies should be on applications in Quadrant 1, which offer high sustainability benefits and align with existing organizational capabilities. The prominence of real-time data collection, inventory management, and energy management aligns with global trends emphasizing operational efficiency and resource optimization. The placement of environmental monitoring (A9) in the low-priority quadrant (4) contrasts with international research that emphasizes green technologies. This discrepancy may be attributed to weaker environmental regulations, lower technological infrastructure, or a primary focus on immediate economic gains within the Iranian context. The prioritization based on the ACM provides a more comprehensive strategy than ranking by attractiveness or capability alone. It allows decision-makers to select applications that not only offer high value but also have a lower implementation risk, considering their specific resources and infrastructure. In conclusion, this research enhances our understanding of IoT as an emerging and transformative technology in the food industry. It assesses various applications from economic, social, and environmental perspectives while evaluating implementation feasibility. The results can serve as a valuable guide for decision-makers and policymakers in the Iranian food industry, enabling a more strategic and effective adoption of IoT technologies. A limitation of this study is the lack of a detailed technical-economic feasibility analysis for each application. Future research should conduct in-depth studies on the selected applications to identify implementation challenges and provide practical solutions.