Supply Chain Scenarios Research Articles

Tracing the propagation of disruptions in supply chain scenarios: A case study of photovoltaics diversification

Diversifying supply chains through reshoring and friendshoring is increasingly proposed as a key strategy for supply security and resilience. Quantitative analyses characterizing to what extend diversification shield countries from supply disruptions remain however scarce. In this paper, we present a methodology to assess the supply risk exposure of countries in different supply diversification scenarios – business-as-usual, reshoring, friendshoring. For each scenario, the propagation of three types of upstream disruptions – supply shortage, export restriction, bilateral trade conflict – is simulated. A fragility ratio metric is introduced to quantify the potential downstream shortages caused by these disruptions. A novel friendshoring modelling approach is also proposed. It consists in determining risk-optimized trade relations based on criteria such as supply concentration and UN voting similarity.The Python-based model is tested on the case of diversified photovoltaics supply chains, e.g., if the US, EU, and India increase domestic production from polysilicon to module. Beyond building up manufacturing capacities, choosing between vertical integration and trade is highly determinant in risk exposure. Each diversification scenario shows pros and cons depending on the country and process considered.Overall, this paper underlines the need for supply risk research to nuance diversification recommendations. It would be particularly helpful to improve indicators accounting for a region's technical and economic ability to supply a given product, and to realistically model the challenges of reshoring.

Net-zero life cycle supply chain assessment of heat pump technologies

In this study, a comparative life cycle assessment of different heat pump technologies based on multiple supply chain scenarios according to the UK market has been conducted. In addition to conventional heat pumps – air-source heat pump (ASHP), ground-source heat pump (GSHP), and water-source heat pump (WSHP) - a novel hydrogen-source heat pump (HSHP) has been studied from an environmental aspect. Different supply chain scenarios have been developed for all technologies based on the leading supplier of heat pump components in the UK. Results showed that the manufacturing and operation of heat pumps are the main causes of the environmental impacts. Among the four technologies studied, ASHP had the highest negative environmental impacts across most categories. HSHP stands out with notably lower carbon emissions compared to other cases. However, in certain impact categories, HSHP exhibited more significant consequences, especially concerning land use and land-related pollution. Regarding CO2 emissions, ASHP is projected to achieve the most substantial reduction, with expected reductions of 23 %, 60 %, and 97 % by 2030, 2040, and 2050, respectively. GSHP, WSHP, and HSHP are also predicted to experience significant CO2 emission reductions of 94 %, 95 %, and 59 % by 2050, aligning with the UK's net-zero goal.

Spatially explicit supply chain for nationwide CO2-to-fuel infrastructure: Data-driven optimization with Gaussian mixture model -based region screening and clustering

This study proposes a novel machine learning-based method for designing spatially explicit supply chains for establishing a nationwide CO2 utilization strategy. The proposed method is capable of considering not only technical feature for CO2-to-fuel conversion, but also the geographical features for facility allocation by integrating spatial data and various sociological information. To develop the proposed method, map polygonization and the ray-casting algorithm for precise spatial data generation are implemented, which identifies and screens out geographically and socially unfavorable terrains for facility allocation. The scenario evaluation method, which includes the Gaussian mixture model-based expectation–maximization algorithm and supply chain economics, such as transportation price and facility capital investment, are considered to calculate a large set of supply chain scenarios. As a case study, the proposed method is applied to determine the optimal supply chain configuration to secure economic feasibility in the Korean CO2 capture and utilization project. The results identify the hydrogen price as the most critical factor in the supply chain economy and show the potential profitability in 2043–2045. This study can promote the nationwide CO2 reduction strategy and the development of a sustainable energy supply system.

Optimizing supply chain management: strategic business models and solutions using SAP S/4HANA

In today's highly competitive and rapidly evolving business environment, optimizing supply chain management (SCM) has become a critical imperative for organizations aiming to enhance operational efficiency, reduce costs, and improve customer satisfaction. SAP S/4HANA, a next-generation enterprise resource planning (ERP) suite, offers a comprehensive and integrated platform for streamlining and modernizing SCM processes. This review explores the strategic business models and solutions facilitated by SAP S/4HANA, focusing on its impact on various facets of supply chain operations, including procurement, manufacturing, logistics, and inventory management. The implementation of SAP S/4HANA enables organizations to leverage advanced analytics, real-time data processing, and intelligent automation to optimize supply chain functions. By providing a unified view of supply chain activities, SAP S/4HANA enhances visibility, enabling proactive decision-making and fostering a more responsive and agile supply chain. Key features such as predictive analytics and machine learning algorithms facilitate demand forecasting and inventory optimization, reducing the risk of stockouts and overstock situations. Furthermore, SAP S/4HANA supports the integration of Internet of Things (IoT) technologies, allowing for real-time monitoring of assets and operations. This connectivity enhances the ability to manage logistics and transportation more efficiently, reducing lead times and improving delivery performance. The platform's ability to harmonize data from various sources also aids in supplier collaboration, ensuring better alignment with suppliers' capabilities and enhancing the overall supply chain resilience. Strategic business models enabled by SAP S/4HANA include just-in-time (JIT) production, which minimizes inventory costs, and demand-driven replenishment, which aligns production schedules with actual customer demand. Additionally, the adoption of digital twin technology within SAP S/4HANA allows for virtual modeling and simulation of supply chain scenarios, facilitating strategic planning and risk management. In conclusion, SAP S/4HANA provides a robust foundation for optimizing supply chain management through its advanced technological capabilities and integrated approach. Organizations adopting SAP S/4HANA can achieve significant improvements in supply chain efficiency, agility, and resilience, positioning themselves for sustained competitive advantage in the dynamic global market. This exploration highlights the transformative potential of SAP S/4HANA in redefining supply chain strategies and operational excellence.

Strategic inventory in semi-conductor supply chains under industrial disruption

The semiconductor industry plays a crucial role in modern technology and the global economy. Over the past few years, it has experienced a series of supply disruptions, leading to significantly adverse impacts on the global economy. Holding strategic inventory is a common strategy adopted by industries to address supply disruptions, but the specific mechanisms and distinctions of its effects from those in traditional supply chain scenarios remain unclear. To explore the impact of strategic inventory in the semiconductor supply chain during industrial disruptions, we establish four game models for the following scenarios: (1) no strategic inventory with no supply disruption (NN), (2) no strategic inventory with supply disruption (ND), (3) strategic inventory with commitment contracts and supply disruption (SCD), and (4) strategic inventory with dynamic contracts and supply disruption (SDD). Firstly, we examine how the supply disruption impacts the overall performance of the semiconductor supply chain and its members. Secondly, we investigate the role of strategic inventory under commitment contracts and dynamic contracts in mitigating the effects of supply disruptions. We compare the preferences of the supply chain parties for the two types of contracts and identify potential equilibrium strategies. Our study reveals several interesting conclusions: (1) Supply disruptions adversely affect the overall performance of the semiconductor supply chain and its members. (2) Even if the manufacturer implements a commitment contract, the retailer will still choose to maintain strategic inventory. (3) When the probability of disruptions is relatively high, the retailer may maintain a higher level of strategic inventory under a commitment contract compared to a dynamic contract. (4) The use of dynamic contract results in superior mitigation of supply disruptions compared to the commitment contract, leading to greater benefits for the entire supply chain and its participants. (5) In dynamic contracts, the retailer may incur losses from holding strategic inventory in specific situations.

Optimizing biomass supply for cofiring at power plants to minimize environmental impact: A case of oil palm empty fruit bunches in West Java

This study addresses the supply flow optimization to reduce global warming in a cofiring system, using the example of oil palm empty fruit bunches converted into pellets for use in power plants in Indonesia. Four oil palm mills, seven pellet plants and five coal power plants in the West Java Region were considered in this study to select the optimal supply chain. The study was performed using a life cycle assessment (LCA) for evaluating impact of single firing of coal and linear programming (LP) for optimizing the impact reduction of cofiring of coal and pellets. Our study indicates that the optimal environmental reduction can be achieved by constructing six of the seven pellet plants, which can substitute up to 1 % of the coal supply in five power plants. Accordingly, the emissions could be decreased by an average of 0.018 kg CO2eq/kWh for the power plants, which is equal to 0.2 Mt CO2eq/y or a 1.57 % reduction per year. By varying the emission factor of the pellet plants, our analysis showed a tolerable range in which the supply would not be affected. The sensitivity analysis at a 1 % cofiring rate showed that the increase in biomass supply from the oil palm mills had the highest reduction, whereas the reduction in transportation emissions provided much less impact. By adjusting the cofiring rate, the impact would not change after the 3 % cofiring rate because of the limited availability of Empty Fruit Bunch (EFB) feedstock. However, if sufficient supply could be provided, the environmental impact would be further reduced significantly. Furthermore, the scenario expansion indicated that only three pellet plants would be required for the minimum economic scale of production, although the environmental impact increased by 0.47 % compared to using six pellet plants. Therefore, this study can be used to determine the optimal supply chain scenarios by constructing an appropriate number of economically feasible pellet plants according to the biomass demand and cofiring rate of the power plant.

Research on Blockchain-driven Agricultural Products E-commerce Supply Chain Innovation under Game Perspective

With the arrival of the big data era, digital technology has achieved an unprecedented development speed, and blockchain, as a representative of digital technology, solves the problem of mutual trust between multiple transaction subjects in different scenarios with the technological advantages of decentralisation, timestamping and non-tampering. Currently, in the agricultural products e-commerce supply chain scenario, there is the problem of credit risk of agricultural products producers and sellers. Aiming at the pain points of agricultural products e-commerce supply chain, this paper analyses from the perspective of game, through blockchain incentive mechanism, and puts forward blockchain-driven agricultural products e-commerce supply chain innovation suggestions.

Experimental study of air cargo temperature variations and its impact on mango quality

An air cargo supply chain for fresh mango was investigated from Thailand to France. Measurements of air temperature, air relative humidity, and fruit (peel) temperature were conducted using thermo-hygrometer data loggers in cartons. In a Unit Load Device (ULD) containing 148 cartons, twenty-seven instrumented cartons were placed at different positions to observe spatial and temporal temperature variations during 12 h cruising. At the destination, mango quality attributes were assessed at different time-points during 15-day storage at 16.1 °C and 21.6 °C. Throughout the supply chain, fruit temperatures ranged from 16.6 °C to 30.5 °C. During cruising, fruit temperatures in upper cartons remained almost constant, whereas base carton temperatures continuously decreased from 26.1 °C to 18.1 °C. Because of a short cruising period in comparison to a storage duration at the destination and proper ambient temperature condition during the experiment, carton position in the ULD had no discernible impact on fruit quality during storage. Fruit stored at 16.1 °C preserved superior fruit quality compared to 21.6 °C storage. For the same storage temperature, better quality of fruits stored in Thailand demonstrated the impact of air shipment on product degradation. The measured temperature and relative humidity evolutions were used as input parameters of a mass loss evolution model. A comparison with the measured data exhibited good agreement (RMSE < 1.8 % mass loss). Considering 10 % mass loss as a critical value, this model allows an estimation of the product shelf life based on a supply chain scenario.

AdaFNDFS: An AdaBoost Ensemble Model with Fast Nondominated Feature Selection for Predicting Enterprise Credit Risk in the Supply Chain

Early warnings of enterprise credit risk based on supply chain scenarios are helpful for preventing enterprise credit deterioration and resolving systemic risk. Enterprise credit risk data in the supply chain are characterized by higher‐dimension information and class imbalance. The class imbalance influences the feature selection effect, and the feature subset is closely related to the predictive performance of subsequent learning algorithms. Therefore, ensuring the adaptivity of feature selection and the subsequent class imbalance–oriented classification model is a key issue. We propose an AdaBoost ensemble model with fast nondominated feature selection (AdaFNDFS). AdaFNDFS uses the FNDFS method in the AdaBoost algorithm to iteratively select features and uses the classifier to evaluate the performance of feature subsets to train the class imbalance–oriented classifier and the best‐matched feature subset, ensuring the adaptivity of feature selection and subsequent classifiers. The further use of the differential sampling rate (DSR) method enables AdaFNDFS to integrate more training models with different knowledge and to obtain higher accuracy and better generalization ability for prediction tasks facing high‐dimensional information and class imbalance. A test using credit risk data from Chinese listed enterprises containing supply chain information demonstrates that the prediction scoring indicators, such as AUC, KS, AP, and accuracy, of the AdaFNDFS are better than those of basic models such as LR, LDA, DT, and SVM and multiple hybrid models that use SMOTE, feature selection, and ensemble methods. AdaFNDFS outperforms the basic models by at least 0.0073 (0.0344, 0.0349, and 0.0071) in terms of the AUC (KS, AP, and accuracy). AdaFNDFS has outstanding advantages in predicting enterprise credit risk in the supply chain and can support interested decision‐makers.

MARLIN Method: Enhancing Warehouse Resilience in Response to Disruptions

Background: Endogenous and exogenous factors impact the operational characteristics of supply chains, affecting wholesale warehouses. The survival of a warehouse is often threatened by disruptive events that alter infrastructure and performance. The emergence of COVID-19 exemplified the need for adaptability in retail goods supply chains, emphasizing the necessity for responding to external shocks. Methods: The MARLIN (Method wArehouse ResiLience dIstruptioN) method, founded on theories and models of resilience engineering is introduced. MARLIN is a practical tool designed to identify key areas requiring intervention in response to disruptive events. An empirical test was conducted in an Italian warehouse. Results: The conducted test yielded tangible results, demonstrating the efficacy of the method. It successfully pinpointed areas necessitating intervention and identified Key Performance Indicators (KPIs) associated with disruptions. The study not only underscores the importance of data collection but also highlights the often-overlooked significance of warehouse management. Conclusions: The study establishes MARLIN as a valuable asset for stakeholders involved in disruption management. Its application has proven instrumental in recognizing areas of intervention and identifying KPIs related to disruptions. Ongoing research endeavors to broaden its applicability across diverse supply chain scenarios, aiming to enhance situational awareness and enable proactive risk assessment through what-if analysis.

Heat pump supply chain environmental impact reduction to improve the UK energy sustainability, resiliency and security

Various heat pump technologies are examined from an environmental perspective using a life cycle assessment approach. The investigated heat pump systems utilize air, ground, and water as their energy sources. Additionally, an innovative heat pump powered by green hydrogen is investigated in this study, to evaluate its environmental impacts and potential to commercialise on a large scale. A range of supply chain scenarios is explored, considering the main suppliers of the UK market. The reshoring heat pump industry and supply chain are evaluated to enhance energy resilience and security within the UK. The findings indicate that the hydrogen-based heat pump presents a promising option for the UK market, offering the advantages of reducing stress on the national grid network and minimizing the environmental impacts associated with the supply chain. Furthermore, a forecasting analysis is conducted based on the UK's net-zero emission plan to provide insight into future developments.

An improved PBFT consensus mechanism with trust value evaluation application in the agricultural product trusted traceability system

Blockchain has the advantages of tamper proof, high reliability and traceability, so it can better support the agricultural product traceability system. However, the agricultural product supply chain has the characteristics of long chain and decentralized production, which makes the application of the combination of blockchain technology and agricultural product traceability have many nodes, large system overhead and other problems. In order to solve the above problems, this paper proposes a Practical Byzantine Fault Tolerance (PBFT) consensus mechanism based on trust value evaluation, and uses this optimization algorithm to propose an architecture model to meet the demand for trusted traceability of agricultural product supply chain, so as to realize the distributed storage and security protection of supply chain data. In order to ensure the effective operation of the blockchain system, this paper studies the consensus mechanism in the blockchain network, designs a trust value evaluation model to calculate the trust value of nodes, and selects trusted nodes to join the consensus group based on the trust value, which ensures the credibility of the consensus nodes. At the same time, the consistency protocol is optimized to reduce the communication overhead in the consensus process. The experimental results show that the consensus mechanism improved in this paper has certain advantages in communication overhead, transaction delay and throughput. Compared with the existing storage model, the model described in this paper has higher security and throughput efficiency, effectively ensures the reliable traceability of data, and can be better applied to the traceability scenario of agricultural product supply chain.

Supply Chain Simulation of Manufacturing Shirts Using System Dynamics for Sustainability

In supply chain management (SCM), goods and services flow from the raw materials stage to the end user with complexities and uncertainty at each stage. Computer modeling and simulation is a particularly useful method to examine supply chain operational issues because it can solve operational complexities that are challenging and time consuming to analyze. Manufacturing companies fear losing valuable time and assets during the manufacturing process; the inaccurate estimation of raw materials, human capital, or physical infrastructure not only leads to monetary loss for the manufacturing unit, but also has a detrimental effect on the environment. The purpose of this paper is to demonstrate that system dynamics modeling (SDM) in sustainable supply chain management (SSCM) can be applied to apparel manufacturing to optimize materials, labor, and equipment usage. Utilizing system dynamics (SD), the manufacturing unit can improve sustainability by reducing materials, labor, and equipment usage, which in turn reduces energy use. In our literature review, we did not identify any study addressing supply chain simulation of the manufacturing of shirts using SDM. We chose shirt manufacturing to demonstrate the model because of its relatively simple manufacturing process. In our study, we conclude that SDM simulation is an efficient way to optimize materials, labor, and equipment in apparel manufacturing. This leads to a more sustainable manufacturing process, as the model simulates different manufacturing supply chain scenarios in a risk-free environment, thereby minimizing waste and resources. Further, the outputs from the STELLA® model can be used as inputs into a subsequent life cycle assessment (LCA) model to determine the quantitative environmental impacts.

Hybrid Quantum-Enhanced Machine Learning for Supply Chain Optimization

Abstract: Modern supply chains face intricate challenges demanding sophisticated optimization strategies.This research presents a pioneering approach, combining quantum computing and machine learning, to revolutionize supply chain optimization. A hybrid framework harmonizes the power of quantum computing, including quantum annealers and gate-based quantum devices, with classical machine learning techniques. This synthesis aims to surmount the limitations of conventional optimization methods. Through rigorous experimentation on real-world supply chain scenarios, our approach demonstrates substantial efficiency gains and enhanced solution quality in comparison to classical methods. The implications are far- reaching, promising substantial cost reductions, streamlined resource allocation, and sustainability improvements for industries relying on supply chain management. Our study contributes to the burgeoning field of quantum machine learning and offers immediate value to supply chain professionals grappling with complexity. As supply chains evolve, our research sets the stage for innovative solutions in the quest for optimization.

Study on Supply Chain Carbon Emission Reduction Measures

A comparative study of three carbon reduction measures in the supply chain, namely carbon tax, government subsidies and carbon trading, is conducted to analyse their advantages and disadvantages, the level of emission reduction and the impacts on supply chain members. Further, through real-life supply chain scenarios such as manufacturing and new energy vehicles, we focus on analysing the emission reduction effects of different government subsidies for manufacturers and retailers. The results show that carbon tax is advantageous in driving economic growth. Different government subsidies stimulate consumption leading to profit growth. The carbon market is easier to implement and control but is still in its infancy and needs to be supported by relevant policies. Manufacturers and retailers in different industrial supply chains have different preferences when it comes to different subsidies, and the right subsidies can lead to higher social welfare and economic efficiency.

Hyperledger Fabric-Based Tea Supply Chain Production Data Traceable Scheme

With the advent of Industry 4.0, blockchain is one of the emerging technologies that can be used in multiple fields, especially in supply chain networks, where it is considered a sustainable concept technology. Because of its decentralization, reliability, transparency, consistency, and traceability, blockchain can challenge existing conventional models in supply chain scenarios. Therefore, this study proposes a framework that integrates the technology of Blockchain 3.0 (Hyperledger Fabric) into the tea supply chain. The concept of data traceability driven by blockchain technology consists of documenting the tea supply chain system, in which the cultivation, processing, and retailing of tea leaves are documented, and optimizing the existing anti-counterfeiting mechanisms. The ECDSA algorithm is used to confirm the identity among the entities located in the Hyperledger Fabric channel, and IPFS technology is introduced to store tea traceability data to solve the problem of data increment in the tea production process. Only the hash value and traceability code of the traceability data are stored in the block. We analyze the relationship between the number of transactions and latency, and we can see the latency time of both write and read transactions. The system tested write transactions and read transactions with a minimum write speed of 0.97 s and a maximum of 6.26 s and a minimum read speed of 0.1 s and a maximum of 4.62 s via Caliper analysis. The experimental results show that the performance analysis sounds good. This study uses distributed theory and service-oriented thinking in its specific application to the tea supply chain system model and takes each entity through a B2B connection to achieve anti-counterfeit traceability in the tea supply chain of a regulatory nature.

Analyzing the hydrogen supply chain for airports: Evaluating environmental impact, cost, sustainability, viability, and safety in various scenarios for implementation

It is a matter of great concern that the aviation industry, which has made significant contributions to the transportation sector, relies heavily on nonrenewable fossil fuels that are causing substantial pollution, leading to the industry's unsustainability. To address this issue, the industry has shifted its focus towards sustainable aviation fuels, with hydrogen fuel showing great promise due to its clean and eco-friendly nature. However, the adoption of hydrogen as an energy carrier in aviation presents several challenges, including the lack of a supply chain comparable to that of kerosene, which is currently the primary fuel used in aviation. The supply chain for hydrogen involves various sub-sections such as production, liquefaction, transport, storage, and distribution. This study aims to analyze the hydrogen supply chains for airports and to develop scenarios for its implementation. The study evaluates the environmental impact, cost, sustainability, viability, and safety in different supply chain scenarios, as well as recommending a comprehensive approach to supply chain development that involves collaboration between industry, government, and academia.

The hydrogen storage challenge: Does storage method and size affect the cost and operational flexibility of hydrogen supply chains?

Hydrogen is seen as a key energy vector in future energy systems due to its ability to be stored in large volumes for long periods, providing energy flexibility and security. Despite the importance of storage in hydrogen's potential role in a zero-carbon energy system, many techno-economic analyses fail to adequately model different storage methods in hydrogen supply chains, often ignoring storage requirements altogether. Therefore, this paper uses a data-driven techno-economic analysis (TEA) tool to examine the effect of storage size and cost on three different 2030 hydrogen supply chain scenarios: wind-based, solar-based, and mixed-source grid electrolysis. For varying storage sizes and specific capital costs, the overall levelised cost of hydrogen (LCOH), including production, storage, and delivery to a constant demand, varies significantly. The LCOH ranges from €3.90–12.40/kgH2, €5.50–12.75/kgH2, and €2.80–15.65/kgH2 for the wind-based, solar-based, and mixed-source grid scenarios respectively, with lower values for scenarios with low-cost storage. This highlights the critical role of low-cost hydrogen storage in realising the energy flexibility and security electrolytic hydrogen can provide.

The effects of subsidy programs in byproduct synergy operations

Byproduct synergy (BPS) is an innovative waste disposal approach. Waste resulting from manufacturers’ production processes is disposed of at processing plants and converted into a recycled byproduct. To reduce the inefficiency of a decentralized supply chain and enhance BPS operations, we consider a subsidy program scheme and analyze different supply chain scenarios: a centralized supply chain without subsidies and a decentralized supply chain with or without subsidies. We discuss different policymaker objectives (i.e., concerned only with social welfare and concerned with both social welfare and environmental impacts). Our results show that when the policymaker cares only about social welfare, the subsidy program always enhances BPS operations. The subsidy program brings more prime products when the BPS’s relative market value is high and less emissions are produced when the BPS’s relative market value is low. When the policymaker cares about both social welfare and environmental impacts, the subsidy program can enhance BPS operations unless the market values of BPS and the prime product are relatively low. Moreover, when the market value of BPS is medium high and the market value of the prime product is low, the policymaker can reduce production emissions by motivating the manufacturer to reduce production quantity of the prime product and encouraging the plant to reduce BPS capacity. When the market value of BPS is low, the policymaker can reduce emissions through efficient production process by only motivating the plant to enhance BPS capacity.

Influence of long-distance air transport conditions on horticultural product quality: Case study of fresh mango shipment from Thailand to France

Four instrumented boxes of mangoes Mangifera indica L. cv. “Nam Dok Mai” were shipped from Bangkok, Thailand, to Paris, France, without control of the boxes’ positions on the aircraft. Two boxes were shipped via a direct flight and two boxes were shipped via an indirect flight. For each box, the internal air temperature, the external air temperature and relative humidity and surface temperature of two fruits were recorded throughout the supply chain from a packing house in Thailand to a storage room in France. A maximum fruit temperature of 33 °C (during transport from an orchard to the packing house) and a minimum fruit temperature of 8 °C (in a cold room of the logistics company following arrival at the airport in France via the indirect flight) were observed. The slight temperature difference between the air and the fruit surface temperature (< 1 °C on average) for both direct and indirect flights suggests that the air was stagnant inside the box and, thus conduction was the main heat transfer mode. Models of product temperature, mass loss, and peel color changes throughout the supply chain were developed. The numerical and the experimental values were in good agreement. These models were able to predict the product mass loss and peel color evolution as a function of a supply chain scenario.