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While autonomization and digitalization solutions appear beneficial within last mile delivery process, the literature on these solutions remain fragmented and distributed across different themes among various research papers. This paper aims to assemble some of the prominent solutions and outline their key characteristics to guide our researchers in future studies. To do so, this paper first extensively investigates the available literature and presents the most prominent solutions in a prioritization and categorization method (PCM) approach. Where these solutions currently stand in the perspective of inclusive last mile 4.0 transition is then discussed in our findings.

The integration of Artificial Intelligence (AI) in manufacturing is often pursued as technology push. In contrast, this paper looks upon the AI-human interaction from a viewpoint that considers both to play an important role in reshaping their individual capabilities. It specifically focuses on how humans can play an important role in enhancing AI capabilities. The introduced concepts are tested in an industrial case study of vision-based inspection in production lines. Furthermore, the paper highlights the need to consider relevant implications for work design for AI integration. The contribution can be of practical value for system developers and work designers in how to target at the design stage the human contribution in AI-enabled systems for production environments.

International audience

Human aspects in collaboration of humans and robots, as common in warehousing, are considered increasingly important objectives in operations management. In this work, we let robots learn about human stress levels based on sensor data in collaborative order picking of robotic mobile fulfillment systems. To this end, we develop a multi-agent reinforcement (MARL) approach that considers human stress levels and recovery behavior next to traditional performance objectives in the reward function of robotic agents. We assume a human-oriented assignment problem in which the robotic agents assign orders and short breaks to human workers based on their stress/recovery states. We find that the proposed MARL policy reduces the human stress time by up 50% in comparison to the applied benchmark policies and maintains system efficiency at a comparable level. While the results may need to be confirmed in different settings considering different types of humans aspects and efficiency objectives, they also show a practicable pathway to control stress levels and recovery for related problems of human-robot collaboration, inside and outside of warehousing.

The world of spare parts may be revolutionized by the advent of additive manufacturing (AM). Thanks to the possibility to manufacture spare parts on-demand, AM has attracted great attention in the last years as a substitute of conventional manufacturing techniques (CM). However, both researchers and practitioners point out two main limitations that might hinder the transition from CM to AM for the manufacturing of spare parts: AM parts’ high production costs and uncertain failure rate. While the former limitation will most likely be overcome in the near future, the latter remains an open issue, so far uninvestigated. We therefore aim to investigate the effect of uncertain failure rate estimates on the optimal inventory level and on the total costs of spare parts management. To do so, we adapt a periodic inventory management policy available in the literature to include failure rate uncertainties and perform a parametrical analysis to investigate their impact varying the mean values of the failure rate, the lead times, and the unitary backorder and production costs. From the results it emerged that the effects of the failure rate uncertainties on the optimal inventory level and on the total costs of spare parts management increases exponentially, leading to a divergence up to 250% for both.

International audience

An increase in e-shopping and (last mile) parcel deliveries has contributed to a rapid growth of urban freight transportation. This generates major impacts on city sustainability and liveability. Current solutions for urban logistics concern road traffic, but multiple Dutch cities have an extensive range of city canals that could be used for freight transportation over water. It was investigated how the city canal network of Amsterdam can be utilised for last mile parcel delivery, and what the related effects are. A MILP formulation of a Two-Echelon, Multi-Depot, Capacitated Vehicle Routing Problem (2E-MD-CVRP) was developed. The model describes a network in which ships transport parcels to pre-determined satellite locations in the city centre, where the parcels are transferred to cargo e-bikes for the last mile of the delivery to the customer. The model was optimised by minimising the total costs, using the Genetic Algorithm (GA). The algorithm was able to find solutions but could not always stay within the constrained search space. Different possible network scenarios were evaluated, describing the consequences with respect to emissions, costs, and traffic flows. The results show promising economic, social, and environmental outcomes for a network with ships and cargo e-bikes instead of delivery vans. A daily and investment cost reduction of 16% and 36% respectively and a 𝐶𝑂2 emission reduction of 78.26% can be realised.

The vision of the smart factory with its interconnected systems is based on the seamless (real-time) integration of data across the information system (IS) landscape. Yet, due to the existence of many legacy systems, this task is far from trivial. In industrial practice, the IS landscape comprises systems of different application functionality which can be characterized as technologically heterogeneous, e.g. transaction processing versus real-time systems. Integrating such systems has always been a major challenge and constraining force for many organizations. This problem is receiving renewed attention in the context of the implementation of the digital twin in manufacturing. Due to its central role in the IS landscape, the digital twin needs to communicate with a number of heterogeneous applications to achieve its full potential, i.e. achieving a complete virtual representation of an asset, process or product. This research analyses the integration requirements from the perspective of the digital twins’ application functionality. In particular, we provide an explicit mapping of the integrations needed between the digital twin and existing information systems (IS) in manufacturing, which serves as a basis to better understand integration issues. These findings provide an explanation for and a conceptualization of some of the challenges that emerge when transforming towards an interconnected smart factory.

Manufacturing execution systems (MES) enable the detailed control of manufacturing operations, i.e. they facilitate digital and integrated shop-floor systems as envisioned by Industry 4.0. Yet, many manufacturing organizations struggle to integrate MES and demarcate it from other information systems (IS) in manufacturing. Therefore, this paper explores how MES can be functionally and technologically distinguished from other IS to determine its (future) role in the IS landscape. To provide an answer, this research applies the conceptualization of IS into five application functionalities and underlying enabling technologies. They are referred to as transaction processing, interactive planning, analytics, document management and process monitoring and control systems. We found that MES merges different types of application functionality into one system through its diverse functional requirements, and therefore can be characterized as technologically heterogeneous, in contrast to other ‘classical’ systems. MES then also takes on a central integrating role in the IS landscape. The findings offer an explanation for the challenges associated with the adoption of MES functionality, and highlight the importance of addressing integration questions in light of Industry 4.0.

With rapid advancements in the application of Industry 4.0 technologies throughout industries, a collection of different views on its potential implications for workers are emerging. Various authors agree that these technologies and their application in manufacturing systems is structurally different compared to current methods of production. Consequently, it is expected that the impact on manufacturing jobs, specifically on the tasks, is profoundly different from what we already know from literature. However, authors often borrow from existing literature to describe changes in work, and are not explicit on how and why Industry 4.0 and the implications is conceptually different. Until now, little research has focused on defining the technical functionalities that give rise to new job designs. This paper therefore focuses on synthesizing the diverging views on the effect of Industry 4.0 on employees’ jobs and specifically aims to understand how the technical changes of the transformation towards a Cyber Physical System in production relate to changes in job design. The central question this paper addresses is: How do the technical changes of the transformation towards a Cyber Physical System impact job design in industrial production? The contribution is an overview of the technical functionalities of Cyber-Physical Systems that are conjectured to change direct and indirect value-adding jobs in industrial production. This model will be used as a basis for further empirical inquiries. Moreover, it provides central points of interests for organizations involved with the design and implementation of Industry 4.0, focusing on job design.

International audience; Book Front Matter of AICT 514

The two volumes IFIP AICT 459 and 460 constitute the refereed proceedings of the International IFIP WG 5.7 Conference on Advances in Production Management Systems, APMS 2015, held in Tokyo, Japan, in September 2015. The 163 revised full papers were carefully reviewed and selected from 185 submissions. They are organized in the following topical sections: collaborative networks; globalization and production management; knowledge based production management; project management, engineering management, and quality management; sustainability and production management; co-creating sustainable business processes and ecosystems; open cloud computing architecture for smart manufacturing and cyber physical production systems; the practitioner's view on "innovative production management towards sustainable growth"; the role of additive manufacturing in value chain reconfiguration and sustainability; operations management in engineer-to-order manufacturing; lean production; sustainable system design for green products; cloud-based manufacturing; ontology-aided production - towards open and knowledge-driven planning and control; product-service lifecycle management: knowledge-driven innovation and social implications; and service engineering.

The two volumes IFIP AICT 459 and 460 constitute the refereed proceedings of the International IFIP WG 5.7 Conference on Advances in Production Management Systems, APMS 2015, held in Tokyo, Japan, in September 2015. The 163 revised full papers were carefully reviewed and selected from 185 submissions. They are organized in the following topical sections: collaborative networks; globalization and production management; knowledge based production management; project management, engineering management, and quality management; sustainability and production management; co-creating sustainable business processes and ecosystems; open cloud computing architecture for smart manufacturing and cyber physical production systems; the practitioner's view on "innovative production management towards sustainable growth"; the role of additive manufacturing in value chain reconfiguration and sustainability; operations management in engineer-to-order manufacturing; lean production; sustainable system design for green products; cloud-based manufacturing; ontology-aided production - towards open and knowledge-driven planning and control; product-service lifecycle management: knowledge-driven innovation and social implications; and service engineering.