Abstract
This document addresses and develops a framework tool to solve reliability issues in the calculation of processing times for components, using their dimensions. This framework was implemented in a real industrial setting, specifically in a multinational company that manufactures highly customizable electric motors according to customer requirements. After identifying the most critical components and their respective process diagrams, a prototype of the proposed framework was developed to calculate production time. Additionally, another prototype was developed to aid in visualizing the company’s workload. As a result of this work, various improvements were observed in the company, including a 42% reduction in the time required to create workflows and an increase in the reliability and dependability of process times. The framework significantly enhanced operational efficiency, streamlined production processes, and provided a robust solution for managing the complexities of custom manufacturing, demonstrating its effectiveness in a real-world industrial environment. Furthermore, this approach has the potential to be adapted for use in other industries facing similar challenges. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
Two essential tasks in production planning and control are the generation and the release of orders to the shop floor. In this study order, generation is based on the Demand Driven Materials Requirement Planning system, while order release is based on the CONstant Work-in-Process system. Although the two systems alone have been extensively studied, their combination has received much less attention. In this paper, we address the problem of sequencing replenishment orders generated by the Demand Driven Materials Requirement Planning system to be released by the CONstant Work-in-Process system. Four pool-sequencing rules have been considered. Two of these are used by Demand Driven Materials Requirement Planning for establishing priorities for order planning and order execution. The other two are the First-Come-First-Served rule and a virtual due date rule. Results of a simulation study show that the rules proposed in the Demand Driven Materials Requirement Planning literature for planning and for execution are not the best options for pool-sequencing, particularly for restricted levels of workload allowed on the shop floor. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
Purpose: This study addresses the development of a variant process planning tool, following the Knowledge-Based Variant Process Planning methodology, applied in a case study and presents the gains achieved. Methodology/Approach: Case study supported by six steps: (1) Feature Analysis, (2) Knowledge Retrieval, (3) Inference, (4) Plan Adaptation, (5) Knowledge Update, and (6) Plan Validation/Optimisation. Findings: The implementation of the Knowledge-Based Variant Process Planning tool led to significant improvements: planner time reduced by 70%, analyst workload by 90%, and process plan errors to 0%. Results show this approach significantly improves process planning in customised production. Research Limitation/Implication: The limitations are associated with the specificity of the case study problem-the electric engine production systems. Originality/Value of paper: This study helps fill the gap in case studies on the Variant Process Planning approach, specifically for electric engine production systems, paving the way for similar companies to adopt Knowledge-Based Variant Process Planning.

Abstract
Demand-Driven Material Requirements Planning (DDMRP) has been proposed as a solution for managing uncertainty and variability in supply chains by combining decoupling, buffer management and demand-driven planning principles. A key element of DDMRP is its inventory replenishment model, which relies on dynamically adjusted inventory buffers rather than fixed stock levels. However, parameterization of these buffers often involves subjective choices, raising concerns about consistency and performance. This paper assesses the DDMRP replenishment model through discrete-event simulation of a multi-echelon, capacity-constrained production system. Two alternative formulations of the safety stock term in the red zone are compared: the original factor-based approach and a revised formula that incorporates measurable variability coefficients. While both safety stock formulations yield similar numerical results, the revised formula enhances transparency and reduces subjectivity. Assessing the impact of introducing a buffer for components in addition to a finished goods buffer further shows that the components buffer can reduce finished goods inventory requirements while maintaining service levels. These findings contribute to a better understanding of the DDMRP replenishment model, offering practical insights for parameter selection and supply chain design.

Abstract
E-commerce growth is driving the need for novel, more sustainable last-mile delivery strategies. One potential strategy is based on setting up a mobile-depot from where last-mile deliveries are conducted using cargo bikes. This research explores the impacts of this strategy through a microscopic traffic simulation based on a medium-sized European city. The strategy was evaluated at three levels: operational (route length and duration), energy consumption, and emissions. The results showed that adopting a last-mile delivery strategy based on a mobiledepot and cargo bikes leads to significant benefits in terms of energy consumption and emission, which decrease by more than 80 %, but imply lengthier (+49 %) and more time-consuming (+14 %) routes compared to a traditional parcel delivery strategy.

Abstract
E-commerce popularity has increased the challenges of parcel deliveries, highlighting the urgency in addressing the sustainability of the last-mile, which is considered the most ineffective part of the supply chain and a source of environmental and social issues. Although different alternative last-mile technologies have already been proposed, they all imply complex trade-offs. This study explores how synergies between delivery technologies could improve last-mile logistics. It evaluates the trade-offs between electric vans, cargo bikes, and parcel lockers, and takes into account factors such as package size, consumer requirement, and urban context. The adoption of a combined delivery strategy suggests environmental and social benefits like the mitigation of congestion and emissions while ensuring advantages for companies due to streamlined operations and offering consumers a broader range of choices.