Abstract
Setting labor standards is an important topic to operational and strategic planning which requires the time studies establishment. This paper applies the statistical method for the definition of a sample size in order to define a reliable cycle time for a real industrial process. For the case study it is considered a welding process performed by a single operator that does the load and unload of components in 4 different welding machines. In order to perform the time studies, it is necessary to collect continuously data in the production line by measuring the time taken for the operator to perform the task. In order to facilitate the measurements, the task is divided into small elements with visible start and end points, called Measurement Points, in which the measurement process is applied. Afterwards, the statistical method enables to determine the sample size of observations to calculate the reliable cycle time. For the welding process presented, it is stated that the sample size defined through the statistical method is 20. Thus, these time observations of the task are continuously collected in order to obtain a reliable cycle time for this welding process. This time study can be implemented in similar way in other industrial processes.

Abstract
This study proposes a framework for short-term production planning of a Portuguese company operating as a tier 2 supplier in the automotive sector. The framework is intended to support the decision-making process regarding a single progressive hydraulic press, which is used to manufacture cold-stamped parts for exhaust systems. The framework consists of two sequential levels: (1) a Mixed-Integer Linear Programming (MILP) model to determine the optimal production quantities per week while minimizing the total cost; (2) a dynamic production sequencing rule for scheduling operations on the hydraulic press. The two levels are combined and implemented in Excel, where the MILP model is solved using the Solver add-in, and the second level uses the optimal production quantities as inputs to determine the production sequence using a dynamic priority rule. To validate the framework, a proposed optimal plan was compared to a real plan executed by the company, and it was found that the framework could save up to 22.1% of the total cost observed in reality while still satisfying demand. To address uncertainties, the framework requires a rolling weekly planning horizon. © 2023, The Author(s).

Abstract
The increased use of smartphones and the COVID-19 pandemic directly influenced the development of remote tools in several areas. In the context of oncology, it was no different, as several studies address health care or services related to mobile devices. Apps aimed at the medical field (m-health) focus directly on monitoring symptoms and improving interaction between health professionals and patients, combined with the convenience of smartphones. In this context, this work aims to address recent studies on the use of m-health in the clinical practice of oncological diseases and report the characteristics of the apps involved. For this, a review of m-health focused on oncology was conducted using the PubMed and Science Direct databases. The investigation was carried out using tools inherent in international databases and was limited to articles published between 2015 and 2022. In total, 34 articles were analyzed, with a higher frequency of publications between 2019 and 2022. The resources observed were patient follow-up, prevention of signs and symptoms, monitoring of treatment and aid in prognosis and diagnosis of patients. It is concluded that a close collaboration among patients, health professionals, and information technology professionals is necessary to optimize symptom recognition and improve patient-professional communication. Although the pandemic has intensified the increase in the use of m-health, its use is expected to increase in the post-pandemic scenario, bearing in mind the changes in social dynamics and the growing dissemination of technologies. © 2023 ITMA.

Abstract
When designing a custom-made product it is important to provide the customer with a budget that resembles the final price. In this work it will be developed a simple application in Python to perform automatic data extraction from computer aided design (CAD) files to estimate multiple linear regression models with the intent of obtaining a more accurate cost estimate. The application will provide an estimate of the amount of raw material needed and time taken to produce a simple inflatable and related products. © 2023 ITMA.

Abstract