Production planning is a critical function in manufacturing aiming at matching demand with capacity and available resources. It is a complex process spanning multiple levels from global volume planning to specific sites and production cells that has a direct impact on the operational efficiency of companies. Given its broad scope and diverse input, production planning is prone to external and internal disturbances calling for agile reactions from the side of planners who have the overall responsibility of creating, maintaining and overseeing the execution of planning activities. Traditional approaches to planning include heavy manual and semi-manual work with static and generic tools that lack agility and multi-level transparency of all necessary planning parameters. Concurrently, digital technologies and smart algorithms are emerging as tools to unlock value from existing process data. The above calls for a need to move towards data-driven production planning to support informed decision making. This paper aims to identify the prerequisites for data-driven production planning and areas of production planning that can benefit most out of a data-driven approach. A case study has been conducted with a manufacturer in the heavy-duty vehicle sector documenting observations and conducting interviews with planners and production leaders. The results shed light on the importance of consolidating foundational prerequisites before transitioning to a data-driven approach in production planning and highlight scenario planning across the planning hierarchy as a high-leverage area for a data-driven approach.
Product realisation process in industrialised house building is often characterized with Engineer-to-order production strategy (ETO). In ETO the design phase is a part of every house building project, and it relies on the combination of process predefinitions and product predefinitions. A challenge with the process part, is to identify, allocate, verify and secure requirements throughout the design process. One set of requirements can be traced to Miljobyggnad, a climate certification system in Sweden, judged from a third-part evaluation of performance for energy, climate, inner and outer environment. Based on indicators the certification has the purpose for house-building companies to reach the goals for a green transition with climate neutral construction processes before 2050. Requirement management becomes a crucial part of the multi-storey housebuilding design phase, where unique layout and other client choices are present in each project. A prerequisite for the systematic requirement management is the definition of underlying information structures. The aim of this study is therefore to identify and describe how information can be structured for systematic management of Miljobyggnad requirements in an industrialized house building context. In a qualitative study, with two industrialised house building companies, three representative house-building projects where followed, mapped, analysed and identified for analyses. One interesting analysis result from the study was how requirements were broken down along the design phase and how domains could support the work for each house-building project. Practical contribution, in how information standards could support Miljobyggnad requirements, was identified align with multidisciplinary roles in the design phase.
As the manufacturing industry moves towards responsible consumption and production, manufacturers are intensifying their focus on enhancing resource efficiency. While resource efficiency in manufacturing is understood conceptually, measuring and managing it is challenging in practice. Though many factories are data-rich, it is often difficult to use data effectively in assessing resource efficiency. With the aim of demonstrating production data's key role in resource efficiency, this study presents an approach for evaluating resource efficiency using readily available production data through a use case of lubricant usage on machining operations. The machining equipment were grouped into four categories based on their lubricant consumption and running time in the results. Results also found significant variations across identical machines. The identification of hotspots and role models, as well as the quantified lubricant efficiency gaps, can be translated into improvement actions, which positively impact sustainability practices in the company's daily operations. Future work will focus on diving deeper into the analysis of individual machines and extending the approach to other resource flows. This research supports an effective application of research-based methods for resource efficiency assessment in industry, encouraging the usage of available production data for more resource-efficient manufacturing.
The evolution of industrial automation has moved beyond fixed, repetitive tasks into the realm of intelligent systems capable of adapting to changing production environments. This shift is particularly evident in the transition from Industry 4.0, emphasizing digitalization and cyber-physical systems, to Industry 5.0, which focuses on human-centered, resilient, and sustainable production. At Volvo GTO, a specific assembly station involves the manual removal of protective caps from valve modules-a repetitive, ergonomically straining task that adds little value. Automating this task aligns with both ergonomic improvements and production efficiency goals. The objective of this study is to develop a vision-based robotic solution capable of detecting and removing protective caps with high flexibility and modularity, even in environments with varying geometries and orientations. The system integrates three major components: a YOLOv11-based vision module with 6DoF pose estimation, a custom-designed gripper tool, and a control system implemented in ROS 2 for robot manipulation and task execution.
Industrial research and innovation programmes are increasingly judged on improving environmental performance without compromising productivity, yet evaluations often understate how project teams explain expected benefits, intended beneficiaries, and scaling mechanisms. Using 29 open-ended responses from Sweden's Produktion2030 portfolio, this study analyses how project teams articulate the programme's underlying change logic. Four recurring dimensions structure these narratives: digitalisation, sustainability, collaboration, and human competence. Digitalisation is described as linking production data to operational decision-making (e.g., monitoring, analytics, traceability, and simulation). Sustainability is framed as embedded in production improvement through resource efficiency, reduced waste, and resilience. Collaboration is presented as essential for diffusion, standardisation, and cross-organisational learning across OEMs, SMEs, suppliers, and academia. Human competence and work environment improvements are described as enabling conditions for adoption. Overall, the findings depict production innovation as a multi-dimensional socio-technical process in which technological, organisational, and human factors reinforce one another. The paper contributes a replicable approach for extracting programme-level theories of change from lightweight project narratives.
