Optimizing Shipping Operations through Real-Time Monitoring and Control: A Decision Support System for Container Stripping Processes

The shipping industry plays a vital role in the global economy, with container shipping being one of the critical components. Shipping companies outline the time for customer stripping days in its contracts. The availability of the containers depends on the stripping days. The stripping days’ tardiness will hinder the availability of the containers. Therefore, it is fundamental for shipping companies to monitor both the actual condition and the contract condition of stripping days to estimate container availability and prompt customers to expedite the unloading process. However, there has yet to be a tool for monitoring the actual and the contract conditions. In this study, we used the recorded container stripping data to analyze container stripping days, tardiness, and other important parameters that indicate the performance and reliability of stripping containers. These data were post-processed and analyzed using data mining methods, and the resulting information was visualized using a dashboard to facilitate quick and effortless monitoring the dashboard in this study depicts post-processed data on container stripping days and tardiness for each port of discharge, cargo, customer, and other parameters. The dashboard was constructed using Google Data Studio. As a result, the dashboard is expected to help companies monitor, control, and analyze customers with high tardiness, allowing companies to act and ensure that the number of available containers after stripping meets demand at a given time

Optimizing Shipping Operations through Real-Time Monitoring and Control

The shipping industry plays a vital role in the global economy, with container shipping being one of the critical components. Shipping companies outline the time for customer stripping days in its contracts. The availability of the containers depends on the stripping days. The stripping days’ tardiness will hinder the availability of the containers. Therefore, it is fundamental for shipping companies to monitor both the actual condition and the contract condition of stripping days to estimate container availability and prompt customers to expedite the unloading process. However, there has yet to be a tool for monitoring the actual and the contract conditions. In this study, we used the recorded container stripping data to analyze container stripping days, tardiness, and other important parameters that indicate the performance and reliability of stripping containers. These data were post-processed and analyzed using data mining methods, and the resulting information was visualized using a dashboard to facilitate quick and effortless monitoring the dashboard in this study depicts post-processed data on container stripping days and tardiness for each port of discharge, cargo, customer, and other parameters. The dashboard was constructed using Google Data Studio. As a result, the dashboard is expected to help companies monitor, control, and analyze customers with high tardiness, allowing companies to act and ensure that the number of available containers after stripping meets demand at a given time

Infected pancreatic necrosis: outcomes and clinical predictors of mortality. A post hoc analysis of the MANCTRA-1 international study

: The identification of high-risk patients in the early stages of infected pancreatic necrosis (IPN) is critical, because it could help the clinicians to adopt more effective management strategies. We conducted a post hoc analysis of the MANCTRA-1 international study to assess the association between clinical risk factors and mortality among adult patients with IPN. Univariable and multivariable logistic regression models were used to identify prognostic factors of mortality. We identified 247 consecutive patients with IPN hospitalised between January 2019 and December 2020. History of uncontrolled arterial hypertension (p = 0.032; 95% CI 1.135-15.882; aOR 4.245), qSOFA (p = 0.005; 95% CI 1.359-5.879; aOR 2.828), renal failure (p = 0.022; 95% CI 1.138-5.442; aOR 2.489), and haemodynamic failure (p = 0.018; 95% CI 1.184-5.978; aOR 2.661), were identified as independent predictors of mortality in IPN patients. Cholangitis (p = 0.003; 95% CI 1.598-9.930; aOR 3.983), abdominal compartment syndrome (p = 0.032; 95% CI 1.090-6.967; aOR 2.735), and gastrointestinal/intra-abdominal bleeding (p = 0.009; 95% CI 1.286-5.712; aOR 2.710) were independently associated with the risk of mortality. Upfront open surgical necrosectomy was strongly associated with the risk of mortality (p < 0.001; 95% CI 1.912-7.442; aOR 3.772), whereas endoscopic drainage of pancreatic necrosis (p = 0.018; 95% CI 0.138-0.834; aOR 0.339) and enteral nutrition (p = 0.003; 95% CI 0.143-0.716; aOR 0.320) were found as protective factors. Organ failure, acute cholangitis, and upfront open surgical necrosectomy were the most significant predictors of mortality. Our study confirmed that, even in a subgroup of particularly ill patients such as those with IPN, upfront open surgery should be avoided as much as possible. Study protocol registered in ClinicalTrials.Gov (I.D. Number NCT04747990)