The floriculture industry involves the cultivation, processing, marketing, and international trade of flowers and ornamental plants (Anumala & Kumar 2021). As a high-value agricultural sector, it is characterised by high perishability, stringent quality requirements, and reliance on global distribution networks (Malakar et al. 2023). The sector has shown substantial growth driven by rising consumer demand, particularly in Europe, North America, and parts of Asia, where flowers are integral to cultural practices (Misra & Baskaran 2020). The global distribution and market share of floral products have evolved with significant trends in the primary export and import regions. The Netherlands, known for its advanced horticultural logistics capacity and export networks, is a prominent player in the global floriculture market (Rodríguez Mañay, Guaita-Pradas & Marques-Perez 2022). Other key contributors include Ecuador, Colombia, Kenya, and Ethiopia (Guaita-Pradas et al. 2023).

Cold chain logistics development has been central to the international expansion of the floriculture industry. Temperature-controlled storage, refrigerated transportation, and improved monitoring systems have enabled flowers to be transported over long distances while preserving their freshness and market value (Karpun 2020). These advances have facilitated the adoption of real-time monitoring and control systems that track temperature and humidity throughout the supply chain, ensuring that flowers remain under optimal conditions from farm to consumer (Van der Vorst et al. 2012). These technologies are essential for maintaining cold chain integrity and minimising spoilage risk (Chen & Lan 2016). In recent years, the focus has shifted towards sustainability and efficiency. The industry has introduced green cold chain practices to reduce the environmental impact of refrigeration and transportation, including the use of energy-efficient refrigeration units, eco-friendly packaging materials, and optimised logistics routes to minimise carbon emissions (Khan et al. 2020; Meneghetti & Monti 2015). These developments underscore the industry’s ongoing efforts to improve flower quality and longevity while addressing environmental and economic challenges. Advances in technology and infrastructure have been instrumental in shaping the modern floriculture supply chain, enabling consumers to receive fresh, high-quality flowers regardless of geographical location (Fan et al. 2021; Saif & Elhedhli 2016).

Cold chain logistics play a fundamental role in ensuring that perishable goods, including fruits, vegetables, and other fresh produce, maintain their quality and safety from farm to final consumer. Through a series of temperature-controlled environments, CCL prevents spoilage and extends product shelf life (Fan et al. 2021). A primary benefit of effective CCL is the reduction of food waste, which is particularly significant given the global challenge of food security and the need to maximise food distribution efficiency (Du Plessis, Van Eeden & Goedhals-Gerber 2024; Liu et al. 2018). Moreover, the CCL is central to ensuring food safety by maintaining the required temperature threshold throughout storage, transport, and handling. Maintaining a consistent cold chain significantly reduces the risk of foodborne illnesses, thereby protecting public health and ensuring that consumers receive safe, high-quality products (Du Plessis, Van Eeden & Goedhals-Gerber 2022; Fan et al. 2021). The economic implications of CCL are also significant. For instance, the South African fruit industry, which exports large quantities of fresh fruit to international markets, benefits greatly from robust CCL systems (Du Plessis et al. 2022; Jiayang et al. 2018). Beyond economic benefits, CCL has environmental implications. Studies have shown that further research is needed to establish the relationship between cold chain duration, energy use, and emissions, underscoring the importance of sustainable practices (Du Plessis et al. 2024; Wu et al. 2019).

For the floriculture industry, CCL ensures that flowers are transported under optimal conditions to preserve their quality through a series of temperature-controlled environments from harvest to the final consumer. The effectiveness of this logistics chain directly affects the vase life of cut flowers, which is a critical quality parameter for consumers. For example, studies have shown that maintaining specific temperature and humidity levels can significantly extend the vase life of cut roses (In et al. 2016). Despite advancements in CCL, the floriculture industry faces several challenges. One of the primary issues is the variability in agricultural practices across different regions, which can affect the quality and consistency of the flowers produced (Wu et al. 2019). Moreover, the floriculture industry must navigate the complexities of international trade, including compliance with regulations and management of supply chain disruptions. The latter has become particularly evident as geopolitical tensions and global events, such as the coronavirus disease 2019 (COVID-19) pandemic, have caused significant supply chain disruptions, leading to delays, increased costs, and reduced flower availability in the market (Ksoll, Macchiavello & Morjaria 2023). To address these challenges, ongoing advancement in research and innovation is essential. The implementation of advanced operational strategies, such as real-time monitoring and data analytics, offers promising avenues for improving the efficiency and reliability of CCL systems (Fan et al. 2024).

Cold chain logistics performance in agricultural cold supply chains encompasses a wide range of factors that contribute to the overall success and sustainability of the system. Conventional performance evaluation methods primarily assess operational efficiency using input–output ratios and are ill-suited for perishable products, as they treat product quality as an exogenous constraint on performance outcomes (Liu et al. 2018). These methods have additional limitations, including an overreliance on qualitative indicators and a lack of comprehensive evaluations. Research on CCL performance for FAPs has been extensive, focusing on various aspects, such as temperature control, quality maintenance, and sustainability (Han et al. 2021; Liu & Zhou 2017; Titlo & Sopadang 2019). Temperature abuse remains a critical issue in cold chain systems. It has been shown that precooled produce retains its shelf life more effectively than non-precooled produce (Conradie, Goedhals-Gerber & Van Dyk 2022). Determining the critical quality parameters at all stages of the CCL is another challenge. For example, in the case of table grapes, real-time temperature monitoring is essential for accurately evaluating quality (Xiao et al. 2016). This highlights the broader need for precise and continuous monitoring throughout the CCL to maintain the quality of perishable products.

However, evaluating the performance of a CCL is complicated by various factors, including shelf life restrictions, seasonality of production, and the need for refrigerated transportation and storage (Titlo & Sopadang 2019). The cold chain is inherently complex, requiring a balance among cost efficiency, quality, and sustainability, three objectives that often conflict because improving product and environmental quality frequently incurs additional costs (Fan et al. 2021). Accordingly, performance evaluation methods must consider complex operations, organisational capabilities, and external collaboration. Continuous monitoring systems, particularly during the transport and handling phases, significantly influence product quality and operational efficiency (Titlo & Sopadang 2019; Xiao et al. 2016). Comprehensive indicator systems encompassing financial performance, service quality, operational efficiency, and organisational capacity enable robust cold chain management (Defraeye et al. 2016; Shashi & Singh 2015; Turan & Ozturkoglu 2022). Comprehensive CCL performance measurement is widely recognised as important, but there is no consensus on what to measure or how to aggregate measures. Several studies focus on specific metrics such as energy consumption, carbon emissions, food waste, service quality, and cost, often treating them as separate performance dimensions rather than merging them into a single score (Kashav et al. 2018; Kumar, Tyagi & Sachdeva 2022; Xie et al. 2022). In agricultural cold chains, key performance indicators (KPIs) such as product waste cost, operating costs, on-time delivery, in-transit loss, and user satisfaction are treated as distinct measures (Kumar, Tyagi & Sachdeva 2023; Li & Qi 2016). In contrast, other studies explicitly construct composite evaluation systems, aggregated using methods such as entropy weighting, fuzzy evaluation, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), or catastrophe progression to yield an overall performance or sustainability score for cold chain enterprises or regions (Gunasekaran & Kobu 2007; Sun, Hu & Liu 2023; Yu & Zhang 2024).

The literature on CCL performance for Fresh Agricultural Products (FAPs) reveals several gaps relevant to the floriculture industry. A significant gap exists: No comprehensive performance measurement model tailored specifically for floriculture cold chains (Gugusa et al. 2026). Although existing studies have developed frameworks for evaluating cold chain performance in sectors such as agriculture, food products, and pharmaceuticals, these models do not fully capture the unique characteristics and requirements of the floriculture industry (Kumar et al. 2022; Rodríguez Mañay et al. 2022). Moreover, existing studies often overlook CCL’s environmental impact: The carbon footprint and energy consumption associated with refrigeration and transportation are critical factors in developing sustainable cold chain practices (Du Plessis et al. 2024; Liu et al. 2018). Furthermore, there is a gap in our understanding of the specific challenges faced by floriculture cold chains in different geographical regions and climates. Seasonality and variability in production, as well as diverse climatic conditions, can significantly affect cold chain performance. Studies addressing these regional and climatic differences are limited, and more localised research is needed to develop tailored solutions for different contexts (Asante et al. 2019; Titlo & Sopadang 2019). Together, these gaps highlight the need for a specialised floriculture CCL performance model that integrates environmental impacts, regional specificity, holistic evaluation, and standardised indicators.

Ethiopia has emerged as a major player in global floriculture, ranking second in Africa and fifth worldwide for flower exports. The sector has grown significantly since the early 2000s, driven by government investment policies, favourable highland agro-climatic conditions, and proximity to air-freight infrastructure at Addis Ababa Bole International Airport (Ethiopian Horticulture Producers Association [EHPEA] 2023). According to the National Bank of Ethiopia, floriculture remains a leading merchandise export sector: In 2023 to 2024, flower exports generated USD 469.9 million, accounting for 12.4% of total merchandise export earnings (NBE 2024). The export flower CCL system involves three main participant categories: Growers, refrigerated truck transport providers, and handling agents, each contributing to the processing and exporting of flowers to diverse global markets. The Ethiopian floriculture industry comprises diverse growers, including large international firms, small and medium enterprises (both local and foreign-owned), joint ventures, and development banks. The Ethiopian flower export CCL begins with growers harvesting flowers that have matured for cutting. Flowers then pass through essential procedures such as pre-cooling, sorting, cleaning, de-leafing, grading, bunching, and quality inspection to meet the export standards.

Flowers are packaged, boxed, and labelled for traceability and transportation. The packaged flowers are then stored at controlled temperatures in the grower’s facilities to maintain their quality until shipment. Refrigerated trucks then transport the flowers, ensuring temperature-controlled transportation from the farm gate to the airport handling facilities. Upon arrival at the airport, trucks await customs clearance, a stage that underscores the importance of regulatory coordination in minimising delays and preserving product quality. The handling agent, represented by the airport perishable cargo division, operates an advanced cold storage facility spanning 3700 m² and capable of accommodating cargo for up to four aircraft. After clearance, the flowers are sent to a receiving area for inspection of temperature records, quantities, and damage. The flowers are then unbatched, consolidated, palletised, and temporarily stored in cold storage facilities at the airport. Finally, the flowers are loaded onto aircraft and transported under controlled conditions to their destinations.

This study adopts a multistage methodological framework integrating a literature review, multi-criteria decision-making, conceptual modelling, and simulation (Figure 1), selected to ensure methodological coherence between the development of the performance measurement framework and system analysis. The overall logic of the research design was as follows: Firstly, the relevant performance dimensions, indicators, and metrics for CCL in FAPs were identified from the literature. Secondly, an expert-based evaluation and prioritisation were undertaken to select the most appropriate performance parameters for evaluating CCL performance in floriculture using the AHP. The AHP was chosen because it has been extensively applied in supply chain and logistics decision problems, and a survey of 15 application papers by Vaidya & Kumar (2006) confirmed supply chain and logistics as among the most prominent application domains. Thirdly, the floriculture export CCL was represented using conceptual modelling. Flowchart and business process model and notation (BPMN) diagrams were used to visualise the flow of flowers, resources, and information across the system. The conceptual model defines the system boundaries, entities, activities, and decision points that serve as the structural basis for simulation modelling. Fourth, the conceptual model was translated into a DES model implemented in AnyLogic 8.9.4 (University Edition) (The AnyLogic Company, Oakbrook Terrace, IL, USA). Discrete Event Simulation was selected because it is well-suited to modelling dynamic, stochastic processes characterised by discrete events, resource constraints, and process variability. Finally, the simulation outputs were analysed using the AHP-informed performance structure.

The first stage comprised a structured review of peer-reviewed literature on CCL performance for FAPs. This stage identified a broad pool of potential performance parameters relevant to the logistics of perishable products. This review supports the development of a hierarchical performance structure comprising dimensions, indicators, and metrics. To select and prioritise the potential performance parameters, expert-based evaluations and judgements were collected using evaluation criteria and pairwise comparisons. The study engaged a panel of 27 domain experts, comprising stakeholders directly involved in floriculture logistics, including farm managers, logistics professionals, representatives from the EHPEA, the Federal Democratic Republic of Ethiopia (FDRE) Ministry of Agriculture, Ethiopian Agriculture Authority, Refrigerated Truck transport service providers, academia, and handling agents for perishable cargo operations. Their assessment was used to select the relevant performance dimensions, indicators, and metrics for the flower export CCL, which assisted in developing a hierarchical performance measurement structure. Pairwise expert judgement was also used to compare the relative importance of the selected dimensions, indicators, and metrics within the hierarchy. These weights provide a conceptual basis for interpreting and analysing simulation outputs from a prioritised-performance perspective.

Conceptual modelling was used to define the system boundaries, entities, process stages, decision points, and information flows. The conceptual model represents the end-to-end flow of flowers from harvesting to export and highlights the operational mechanisms that affect the system performance. The conceptual model was developed using the BPMN representation. The BPMN captured the sequential operational logic of the CCL, organised by actor lanes (Market, Grower or Exporter, and Handling Agent), showing material flow, information flow, and explicit coordination requirements. The conceptual model was validated through structured focus group discussions with seven participants from each major stakeholder group, following the procedure recommended by Robinson (2020). The participants reviewed the process representations, drawing on their operational experience, and confirmed the scope and structure of the model.

Data collection followed the multi-stage design of the study. Initially, a structured review of 48 peer-reviewed studies was conducted to identify performance dimensions, indicators, and metrics relevant to CCL performance for FAPs. These parameters were subsequently evaluated and prioritised through expert judgments using a pairwise comparison questionnaire based on Saaty’s AHP scale. The expert panel comprised professionals from flower-growing firms, refrigerated transport providers, handling agents, academia, the EHPEA, the FDRE Ministry of Agriculture, and the Ethiopian Agriculture Authority. Empirical data for conceptualisation and simulation parameterisation were collected through direct observation, semi-structured interviews, and historical operational records from flower growers, refrigerated transport providers, and handling agents for perishable cargo. These data enabled an accurate representation of the operational processes and system interactions. Data were collected from eight growers representing different operational scales within the Ethiopian floriculture export sector. The data points include shipment-level information on throughput, service times, transportation times, temperature, cost, and energy-related variables.

To represent the operational uncertainty and variability within the system, stochastic probability distributions were fitted to the key input variables using empirical observations. Beta distributions were used to model the handling-agent processing time and temperature variables because these variables exhibited bounded behaviour within the operational minimum and maximum limits. In addition, normal distributions were applied to relatively stable operational processes, such as flight time and cold storage temperature, because these variables fluctuate around constant mean values with limited variability. Triangular distributions estimated through expert judgment were used for harvesting and for selected service-time variables, where empirical observations were unavailable. This distribution is appropriate for representing uncertain processes using minimum, most likely, and maximum estimates. The daily order size was represented using a Poisson distribution to reflect the demand variability associated with seasonal and demand-driven export patterns. The input data and fitted distributions were integrated into the DES model using the AnyLogic database functionality to support a realistic system representation.

The conceptual model was translated into a DES model using AnyLogic 8.9.4 (University Edition). Discrete Event Simulation was selected because the floriculture cold chain is governed by discrete operational events occurring over time, including arrivals, service completions, loading activities, transport time, inspections, and storage transitions. The modelling approach also allows for the explicit representation of resource constraints, queue formation, process variability and time-dependent quality exposure. The simulation model was designed to reproduce the physical and decision logic of export CCL and to generate output data for each KPI included in the performance framework. The DES model was validated using both qualitative and quantitative approaches. The model’s validity was qualitatively assessed through an industry expert review to verify the logic, structure, and realism of the DES. Historical process knowledge and observed operational patterns were used to assess the plausibility of the simulation representation. In addition, quantitative validation was conducted by comparing the simulation output with independently collected historical performance data. Three performance metrics were tested across all eight growers: Daily throughput (total boxes shipped to the international market), total shelf life consumed (hours), and total process time (hours). All tests accepted the null hypothesis, which states that the simulation output and historical records follow the same distribution, constituting statistical validation of the model across the performance dimensions. The simulation experiment was configured as a terminating simulation executed for 720 h, representing 30 days of consecutive cold chain operations. A pilot experiment with 10 replications was conducted to estimate the output variance and determine the required number of replications based on the confidence interval precision. Accordingly, 30 independent replications were conducted using random seeds to ensure statistical reliability.

This study did not involve human participants as research subjects. Data were collected from commercial operational records and expert consultations with participants willing to share their professional experiences. The research was registered with the Liverpool John Moores University, University Research Ethics Committee (UREC) under the Minimal Risk Registration Number 23/MME/23 and approved by the Research Governance Assessment. All participating professionals and organisations provided written informed consent to the use of their operational data in anonymised form. Confidentiality was maintained throughout the study by removing identifiers that could link operational data to specific organisations.

The performance measurement framework developed in this study (Figure 2) adopts a hierarchical multi-criteria structure, integrating insights from literature synthesis and expert-based evaluation to determine parameters relevant to CCL performance in the floriculture context. This structured approach reflects the systemic progression from the conceptual identification of performance parameters to quantitative prioritisation, ensuring both theoretical and empirical validity. The framework is organised into three hierarchical levels: performance dimensions, indicators, and metrics. At the highest level, the framework captures the broad domains of performance: operational, economic, and sustainability dimensions. The second level refines these dimensions into measurable performance indicators, including cost, efficiency, quality, timeliness, and environmental impacts. These indicators serve a critical bridging function by translating dimensions into operational constructs. The third level operationalises these constructs through specific, quantifiable metrics, such as logistics cost, energy cost, throughput, resource utilisation, process time, waiting time, shelf life, temperature degree hours (TDH), CO₂ emissions, and energy consumption. This hierarchical structure provides conceptual clarity and ensures that the simulation outputs are interpreted within an analytically coherent framework.

The AHP approach enabled the systematic evaluation and prioritisation of these dimensions, indicators, and metrics. Expert judgements were aggregated using the geometric mean method, and internal consistency was validated using Saaty’s Consistency Ratio (CR) (Saaty 1980). The synthesis of the AHP results revealed a clear and decision-relevant hierarchy of priorities across the three analytical layers, with all CRs falling well below the conventional 0.10 threshold (CR = 0.033 for dimensions, 0.028 for indicators, and 0.021 for metrics), confirming that expert judgements are internally coherent and that the derived weights warrant interpretive confidence (Table 1). The normalised pairwise comparison matrix and derived priority weights for the performance dimensions assign the highest priority to Operational Performance (w = 0.620), followed by Sustainability Performance (w = 0.270) and Economic Performance (w = 0.110). The AHP also revealed the aggregated priority weights for the five performance indicators. Quality (w = 0.401) and timeliness (w = 0.243) together accounted for 68.5% of the total indicator level weights. Cost (w = 0.136), environmental (w = 0.146), and efficiency (w = 0.074) occupy smaller portions of the expert preference distribution. At the metric level, shelf life (w = 0.232) and TDH (w = 0.184) accounted for 50.7% of the total metric weight. The remaining eight performance metrics shared the remaining 49.3% progressively, from waiting time (w = 0.137), CO₂ emissions (w = 0.105), logistics cost (w = 0.092), resource utilisation (w = 0.088), process time (w = 0.059), energy cost (w = 0.048), energy consumption (w = 0.033), and throughput (w = 0.022).

The AHP results show that operational performance received the highest overall weight, indicating that experts view process reliability and product preservation capacity as the main concerns in floriculture logistics. This is relevant in floriculture, where even minor deviations in temperature and delays can degrade quality. At the indicator level, the prioritisation of quality and timeliness reinforces the centrality of the biological preservation process. The quality indicator captures the physiological condition of flowers, whereas timeliness reflects the temporal dimensions of logistics operations. At the metric level, the prominence of shelf life and TDH demonstrates the framework’s alignment with the domain-specific performance requirements. These results are theoretically and practically meaningful because they align the performance framework with the biological sensitivity of cut flowers and the time-critical nature of perishable export logistics.

Developing a conceptual model is the initial step in creating an effective DES. According to Robinson (2020), conceptual modelling is the most critical component of simulation studies because it ensures appropriate model complexity. The conceptual model framework for assessing CCL performance in the floriculture industry involves several critical components. This framework integrates theoretical perspectives and empirical data to create a simulation model that effectively evaluates the CCL processes. The framework must account for the characteristics of floriculture products, which are highly perishable and sensitive to temperature fluctuations. The BPMN diagram shown in Figure 3 presents a structured representation of the CCL process for flower export. It is organised into three primary lanes: Market, Grower/Exporter, and Handling Agent. The model captures the end-to-end operational flow by integrating order management, post-harvest handling, refrigerated transportation, quality control, and final deliveries.

Within the Market Lane, the process begins when an order enters a queue for fulfilment. At this level, exceptions such as flowers failing quality inspection, unfulfilled orders, overstayed boxes, and delays associated with high-temperature exposure were explicitly identified. These exception pathways reflect operational risks and performance failures, influencing whether orders are fulfilled. In the Grower/Exporter Lane, the physical flow of flowers begins with the availability of products for harvesting, followed by cutting and post-harvest processing, including packing. The process then reaches an exclusive gateway that evaluates whether the flowers meet the export quality standards. The second decision point evaluates the waiting time in the cold storage. Prolonged storage time (more than 48 h) triggers the diversion of flowers to the local market. For the accepted flowers, the process proceeds with loading them onto refrigerated trucks and transporting them to the airport for export. A message flow indicates the coordination required for cold truck services, highlighting the integration of logistics operations across actors. The Handling Agent Lane captures downstream logistics operations at airports and during international transport. Upon the arrival of trucks, a gateway evaluates the timeliness of shipment arrival, where delays result in diversion to the local market. For timely shipments, to ensure regulatory compliance through customs clearance, flower boxes are unloaded and subject to temperature measurements upon receipt. This stage introduces monitoring mechanisms that record temperature conditions and feed information to upstream actors. The process continues through palletising, cold storage, loading, temperature-controlled international flights, and delivery to the destination. The BPMN model integrates material flow, information flow, and control mechanisms. This highlights the interplay between operational efficiency and quality control. The inclusion of multiple gateways and feedback loops underscores the importance of temperature control, process time management, and regulatory compliance in maintaining product quality and ensuring effective cold-chain performance in the floriculture export sector. This presentation provides an important basis for CCL performance analysis by identifying where bottlenecks, delays, and environmental deviations are likely to affect the system. It also demonstrates the value of combining process mapping with simulation, as the conceptual model clarifies the logic and decision structure before stochastic experimentation begins.

Discrete event simulation is a valuable tool for analysing and improving the CCL of FAPs. This approach is well-suited for modelling the dynamic and stochastic characteristics of supply chains, enabling decision-makers to evaluate alternative scenarios and strategies (Bhardwaj & Agrawal 2024). Through DES models, product flow within a cold chain can be effectively simulated to identify bottlenecks, evaluate performance, and optimise resource allocation (Zhu et al. 2014). Previous studies have used DES to examine cold chain aspects such as temperature control, transportation, and storage. For instance, a novel route optimisation for the CCL distribution of FAPs was developed using a discrete-event simulation (Jiayang et al. 2018). This approach is particularly useful for analysing the performance of CCL, where the timing and sequence of events are critical for maintaining the quality of perishable products. In practical terms, the model enables comparison of grower-level outcomes across multiple KPI categories while preserving the complexity of the system. An additional strength of the model is its ability to integrate operational and quality variables. Rather than assessing throughput, cost, or temperature in isolation, the model enables determination of how process time, storage exposure, and transportation behaviour interact to influence shelf life, cost, and emissions.

Simulation-driven performance analysis of CCL for flower exports offers insights into multiple KPIs. This study examined critical CCL performance metrics, including throughput, processing time, waiting time, logistics operational costs, energy costs, temperature degree-hours, shelf life, energy consumption, and CO₂ emissions. The subsequent discussion addresses the main findings and insights derived from this comprehensive evaluation.