Abstract
This paper examines the concept of learning factories and their role in addressing contemporary challenges in the production sector. Learning factories integrate learning and production environments, offering hands-on experiences to develop essential competencies for modern manufacturing. Originating from initiatives like the Germany's "Lernfabriken" in the late 1980s and the National Science Foundation's funding in the 1990s, learning factories have gained global prominence. They serve as platforms for research, education, and workforce development, attracting students and workers from diverse sectors. Examples from Europe, the United States, and China illustrate various approaches to leveraging learning factories for industrial advancement and skill development. Overall, learning factories play a vital role in fostering innovation, enhancing competitiveness, and driving economic growth in the manufacturing sector. © 2024 The Authors.

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Purpose: This paper intends to evaluate the impact of Toyota Way (TW) focused activities on operational performance and its connection to sustainability and longterm success. Methodology/Approach: Three theoretical-practical activities were implemented in a real pickup assembly plant. Performance was assessed through the recording of standard documentation before and after implementation, direct observation at the gemba, and anonymous qualitative surveys of those involved. Findings: Results show how TW enhances workers' skills alongside TPS through experiential learning, fostering continuous improvement with minimal or no financial investment and creating value iteratively and exponentially. However, it had a limited impact on environmental factors. TW emerges as a critical link between short-term operational performance and long-term sustainable growth. Research Limitations/Implications: The sample is restricted to a single assembly plant in Portugal. The surveys involved between 5 and 13 respondents per activity. Originality/Value of paper: In contrast to TPS and lean manufacturing, current literature on TW is limited, often outdated, and lacks clarity regarding its Japanese and American interpretations. Furthermore, few studies emphasise the human element as a driver of company growth-a factor often overlooked by companies. Category: Case study

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This paper studies a real world dedicated parallel machine scheduling problem with sequence dependent setups, different machine release dates and additional resources (PMSR). To solve this problem, two previously proposed models have been adapted and a novel objective function, the minimisation of the sum of the machine completion times, is proposed to reflect the real conditions of the manufacturing environment that motivates this work. One model follows the strip-packing approach and the other is time-indexed. The solutions obtained show that the new objective function provides a compact production schedule that allows the simultaneous minimisation of machine idle times and setup times. In conclusion, this study provides valuable insights into the effectiveness of different models for solving PMSR problems in real-world contexts and gives directions for future research in this area using complementary approaches such as matheuristics.

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This work was developed to improve the costing process of new products within the Product Development Department of a furniture manufacturer. It consisted of creating a parametric cost estimation model based on applying simple and multiple linear regressions, considering the existing data of the products produced and their respective costs. The proposed model considers the cost estimation of creating a product that covers the materials and operations costs. The suitability of the different independent variables was studied by applying simple and multiple linear regressions. A set of functions that return an estimate of the cost as a function of these predictor variables was obtained. The model built with the functions obtained provides the materials and operations cost estimation. The results indicated that 75% of the tests performed show an estimation error of less than 2% in the total cost of a product. Incorporating this model in a tool with the purpose of cost estimation brings the ability to predict prices faster, improving the internal process of obtaining costing and enhancing the analytical capacity of the team in the relentless pursuit of cost minimization and value creation.

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