Small and medium-sized enterprises (SMEs) are central to economic development and innovation but often encounter significant obstacles in adopting advanced technologies such as artificial intelligence (AI). This study examines the potential of AI to address these challenges and enhance the competitiveness of SMEs. In a multiple case study, the research integrates a review of the relevant literature, in-depth case analyses, and semi-structured interviews to identify key barriers to AI adoption, including deficiencies in technical skills, organizational inertia, limitations in data infrastructure, and constrained resources. The study critically evaluates the transformative role of AI in improving decision-making processes, operational efficiency, and strategic adaptability in SMEs. Strategies for mitigating adoption barriers are proposed, emphasizing the importance of targeted training programs, fostering an innovation-oriented organizational culture, prioritizing robust data governance, and utilizing accessible AI tools. While the study acknowledges limitations such as the small sample size and the potential for interpretative bias, its findings contribute to the academic discourse on technology adoption in SMEs. The research provides a framework for understanding AI's role in SME contexts and offers directions for future empirical investigations into the intersection of AI and small business sustainability.

Artificial Intelligence (AI) is rapidly changing product development cross industries. Companies are using AI to optimize processes, make data-driven decisions, and create more personalized customer experiences. However, the integration of AI in product development also presents challenges, including high implementation costs, data security concerns, ethical considerations, and the risk of bias in AI decision-making. To mitigate these risks, companies need to carefully evaluate AI strategies, balance potential benefits with potential risks, and address ethical considerations. As AI technology continues to evolve, its strategic and ethical integration into product development will be crucial for businesses to maintain a competitive edge and drive innovation.

Research in warehouse optimization has gotten increased attention in the last few years due to e-commerce. The warehouse contains a waste range of different products. Due to the nature of the individual order, it is challenging to plan the picking list to optimize the material flow in the process. There are also challenges in minimizing costs and increasing production capacity, and this complexity can be defined as a multidisciplinary optimization problem with an IDF nature. In recent years the use of parallel computing using GPGPUs has become increasingly popular due to the introduction of CUDA C and accompanying applications in, e.g., Python. 

In the case study at the company in the field of retail, a case study including a system design optimization (SDO) resulted in an increase in throughput with well over 20% just by clustering different categories and suggesting in which sequence the orders should be picked during a given time frame. 

The options provided by implementing a distributed high-performance computing network based on GPUs for subsystem optimization have shown to be fruitful in developing a functioning SDO for warehouse optimization. The toolchain can be used for designing new warehouses or evaluating and tuning existing ones. 

The freight transport system is subject to delays and disturbances, which influence investment and planning decisions made by governments and infrastructure authorities. Traditionally relying on Cost Benefit Analysis (CBA) they are dependent on correct and up-to-date input data. So far, little success has been reached in estimating the effects of disturbances for freight.

This paper aims to contribute to the understanding of disturbances in freight transport by reviewing and classifying the effects occurring due to transport time variability (TTV) and to suggest a calculation model to estimate the value of transport time variability (VTTV). In order to validate the model and its usability it was successfully tested in a case study for a large Swedish retail company.

The effects of delays can be divided into four main types: System Killers, Catastrophic Events, Expected Risks, and Contingencies, of which the last two are relevant for VTTV. The model applies these in a two-step cost function with a fixed and variable part, building on previous studies of VTVV for passenger transport based on the scheduling utility approach. A main theoretical result is that the estimation of VTTV is derived mathematically independently of which measure that is chosen for the quantification of TTV.

The freight transport system is subject to delays and disturbances, which influence investment and planning decisions made by governments and infrastructure authorities. Traditionally relying on Cost Benefit Analysis (CBA) they are dependent on correct and up-to-date input data. So far, little success has been reached in estimating the effects of disturbances for freight. This paper aims to contribute to the understanding of disturbances in freight transport by reviewing and classifying the effects occurring due to transport time variability (TTV) and to suggest a calculation model to estimate the value of transport time variability (VTTV). In order to validate the model and its usability it was successfully tested in a case study for a large Swedish retail company. The effects of delays can be divided into four main types: System Killers, Catastrophic Events, Expected Risks, and Contingencies, of which the last two are relevant for VTTV. The model applies these in a two-step cost function with a fixed and variable part, building on previous studies of VTVV for passenger transport based on the scheduling utility approach. A main theoretical result is that the estimation of VTTV is derived mathematically independently of which measure that is chosen for the quantification of TTV.