Future of supply chain planning: Closing the gaps between practice and promise

The purpose is to develop a research agenda for supply chain planning (SCP) relevant to practice. We critically evaluate academic literature on SCP in order to understand how problems are addressed in their particular context, what the outcomes are, and the mechanisms producing the observed outcomes. Four categories of SCP are studied: sales and operations planning (S&OP), supply chain master planning, supply chain materials management, and collaborative materials management. We introduce the concept of enabling mechanisms to identify specific innovations in materials management and production management that can facilitate the future improvement of SCP. The critical evaluation of current SCP theory presents very limited results that are of practical relevance. SCP is not presented as an intervention and the results are not in a form that is actionable for practitioners. The body of literature is almost absent in addressing problems according to context, it presents limited evidence of intended outcomes, and it fails to identify unintended outcomes. As a consequence, research is unable to bolster theoretical understandings of how outcomes – both intended and unintended – are achieved. In our forward-looking research agenda we leverage our understanding of the enabling mechanisms in order to propose research to make mature S&OP and novel types of SCP implementable. The paper is an example of a structured approach to developing a research agenda that is relevant to practice and can be used more widely in logistics and supply chain management. This paper presents a research agenda to close the gap between practice and promise in SCP. We operationalize what constitutes practical relevance for an established field of research

Research Mode and Contribution in Interorganizational Information Systems Research

We develop a model to analyze the body of knowledge of the information systems (IS) field where research accumulates through the interplay of different modes: discovery, prescriptive, and evaluation. The paper proposes five signature contributions: 1) descriptions of discovery and exploration, 2) elaborations of IS-based means and means-ends propositions, 3) discussions of IS-based designs, 4) explanations of the impacts and impact mechanisms of IS, and 5) discussions of organizational theories of IS-phenomena. We argue that each of these contributions plays an important role in the accumulation of the body of knowledge. In particular, we call for a balance in approaches producing these different contributions. Results from analyzing two samples of published interorganizational information systems (IOS) research in high-tier information systems journal outlets from 1982-2010 supports the applicability of the framework as a useful way to categorize the research stream. In line with prior suggestions, we also found an increased tendency towards explanatory organizational theories in that less work has focused on discovering new practices, developing means, and evaluating their uses. Recent interest in academically rigorous design science research offers a welcome addition to the body of IS research that could broaden its base and enrich its content and contributions

Real-time End-to-End Federated Learning: An Automotive Case Study

With the development and the increasing interests in ML/DL fields, companies are eager to utilize these methods to improve their service quality and user experience. Federated Learning has been introduced as an efficient model training approach to distribute and speed up time-consuming model training and preserve user data privacy. However, common Federated Learning methods apply a synchronized protocol to perform model aggregation, which turns out to be inflexible and unable to adapt to rapidly evolving environments and heterogeneous hardware settings in real-world systems. In this paper, we introduce an approach to real-time end-to-end Federated Learning combined with a novel asynchronous model aggregation protocol. We validate our approach in an industrial use case in the automotive domain focusing on steering wheel angle prediction for autonomous driving. Our results show that asynchronous Federated Learning can significantly improve the prediction performance of local edge models and reach the same accuracy level as the centralized machine learning method. Moreover, the approach can reduce the communication overhead, accelerate model training speed and consume real-time streaming data by utilizing a sliding training window, which proves high efficiency when deploying ML/DL components to heterogeneous real-world embedded systems

EdgeFL: A Lightweight Decentralized Federated Learning Framework

Federated Learning (FL) has emerged as a promising approach for collaborative machine learning, addressing data privacy concerns. However, existing FL platforms and frameworks often present challenges for software engineers in terms of complexity, limited customization options, and scalability limitations. In this paper, we introduce EdgeFL, an edge-only lightweight decentralized FL framework, designed to overcome the limitations of centralized aggregation and scalability in FL deployments. By adopting an edge-only model training and aggregation approach, EdgeFL eliminates the need for a central server, enabling seamless scalability across diverse use cases. With a straightforward integration process requiring just four lines of code (LOC), software engineers can easily incorporate FL functionalities into their AI products. Furthermore, EdgeFL offers the flexibility to customize aggregation functions, empowering engineers to adapt them to specific needs. Based on the results, we demonstrate that EdgeFL achieves superior performance compared to existing FL platforms/frameworks. Our results show that EdgeFL reduces weights update latency and enables faster model evolution, enhancing the efficiency of edge devices. Moreover, EdgeFL exhibits improved classification accuracy compared to traditional centralized FL approaches. By leveraging EdgeFL, software engineers can harness the benefits of federated learning while overcoming the challenges associated with existing FL platforms/frameworks

Intelligent Products: Shifting the Production Control Logic in Construction (With Lean and BIM)

Production management and control in construction has not been addressed/updated ever since the introduction of Critical Path Method and the Last Planner® system. The predominant outside-in control logic and a fragmented and deep supply chain in construction significantly affect the efficiency over a lifecycle. In a construction project, a large number of organisations interact with the product throughout the process, requiring a significant amount of information handling and synchronisation between these organisations. However, due to the deep supply chains and problems with lack of information integration, the information flow down across the lifecycle poses a significant challenge. This research proposes a product centric system, where the control logic of the production process is embedded within the individual components from the design phase. The solution is enabled by a number of technologies and tools such as Building Information Modelling, Internet of Things, Messaging Systems and within the conceptual process framework of Lean Construction. The vision encompasses the lifecycle of projects from design to construction and maintenance, where the products can interact with the environment and its actors through various stages supporting a variety of actions. The vision and the tools and technologies required to support it are described in this pape

Release of retained oaks in Norway spruce plantations. A 10-year perspective on oak vitality, spruce wood production and ground vegetation

This study explores the decade-long effects of release cutting around old retained oaks (Quercus robur L.) in a Norway spruce (Picea abies L. Karst) stand that was 33 year old when thinned. The impacts on both nature conservation values and spruce wood production were evaluated in a randomized block design. To release oaks from competition, stems of Norway spruce were cut around 33 oaks, in three different treatments: high release (HR), medium release (MR) and no release (NR). Trees within a circular sample plot (15 m radius from the oak) were measured at time of treatment and 10 years after. The treatment effects on stand development, oak vitality and understory vegetation were evaluated after ten years, using tree diameter, height measurements, oak crown and tree structure estimates as well as ground vegetation surveys. Release cutting did not impact spruce production within the sample plot, and given that there were no other obvious sources of spruce suppression in the stand, we speculate that release cutting has little to no impact at the stand scale. Oak crowns in the control plots (NR) became smaller after ten years, while the crowns expanded and colonized the gap in the release treatments. Simultaneously, the amount of dead wood in the crown increased among oaks in the control treatment, indicating dieback. Cover and species richness of vascular plants in the understory were significantly higher in the HR and MR treatments compared to NR. These results suggest that the creation of relatively wide gaps (greater than 2 m) around retained oak crowns is one efficient approach to maintain their conservation values in a spruce dominated stand on a longer time frame. This will allow oaks to expand their crowns, increase their vitality and increase species richness and diversity of plants under the canopy. The economic loss of creating large gaps instead of no gaps may be negligible since the overall spruce production was not affected within 15 m of each oak

Game-based learning in an Industrial Service Operations Management Course

[EN] This study explores how a game-based approach supports students’ learning in a graduate course on industrial service operations management. Aalto Manufacturing Game (AMG) has been played for several years as a part of an Industrial Management course to provide students with a realistic view of industrial services and asset management. The game illustrates supply chain dynamics and asset management challenges, with a focus on the quality deterioration problem in service provision (Oliva & Sterman, 2001). In this paper, we evaluate the effect of AMG on participants’ learning based on game session feedback and written exam answers. We also evaluate the game as a learning experience trough feedback, observations, and interviews. The findings suggest that the gamified version of teaching provides students with the opportunity to learn by doing while having fun in the class. The game enables participants to socially construct knowledge, raising the effectiveness of teaching supply chain challenges by simulating real world problems from different perspectives of actors involved in operations. Based on our research we argue that the game enhances the learning experience through emotionally engaging students in the activity. To this end, the learning objectives should be embedded in the game dramaturgy.http://ocs.editorial.upv.es/index.php/HEAD/HEAD18Tetik, M.; Öhman, M.; Rajala, R.; Holmström, J. (2018). Game-based learning in an Industrial Service Operations Management Course. Editorial Universitat Politècnica de València. 837-845. https://doi.org/10.4995/HEAD18.2018.8095OCS83784

An architecture for enabling A/B experiments in automotive embedded software

A/B experimentation is a known technique for data-driven product development and has demonstrated its value in web-facing businesses. With the digitalisation of the automotive industry, the focus in the industry is shifting towards software. For automotive embedded software to continuously improve, A/B experimentation is considered an important technique. However, the adoption of such a technique is not without challenge. In this paper, we present an architecture to enable A/B testing in automotive embedded software. The design addresses challenges that are unique to the automotive industry in a systematic fashion. Going from hypothesis to practice, our architecture was also applied in practice for running online experiments on a considerable scale. Furthermore, a case study approach was used to compare our proposal with state-of-practice in the automotive industry. We found our architecture design to be relevant and applicable in the efforts of adopting continuous A/B experiments in automotive embedded software.Comment: To appear in the 45th Annual IEEE Conference on Computers, Software and Applications (COMPSAC'2021

Autonomous Navigation and Configuration of Integrated Access Backhauling for UAV Base Station Using Reinforcement Learning

Fast and reliable connectivity is essential to enhancing situational awareness and operational efficiency for public safety mission-critical (MC) users. In emergency or disaster circumstances, where existing cellular network coverage and capacity may not be available to meet MC communication demands, deployable-network-based solutions such as cells-on-wheels/wings can be utilized swiftly to ensure reliable connection for MC users. In this paper, we consider a scenario where a macro base station (BS) is destroyed due to a natural disaster and an unmanned aerial vehicle carrying BS (UAV-BS) is set up to provide temporary coverage for users in the disaster area. The UAV-BS is integrated into the mobile network using the 5G integrated access and backhaul (IAB) technology. We propose a framework and signalling procedure for applying machine learning to this use case. A deep reinforcement learning algorithm is designed to jointly optimize the access and backhaul antenna tilt as well as the three-dimensional location of the UAV-BS in order to best serve the on-ground MC users while maintaining a good backhaul connection. Our result shows that the proposed algorithm can autonomously navigate and configure the UAV-BS to improve the throughput and reduce the drop rate of MC users.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Statistical Models for the Analysis of Optimization Algorithms with Benchmark Functions

Frequentist statistical methods, such as hypothesis testing, are standard practice in papers that provide benchmark comparisons. Unfortunately, these methods have often been misused, e.g., without testing for their statistical test assumptions or without controlling for family-wise errors in multiple group comparisons, among several other problems. Bayesian Data Analysis (BDA) addresses many of the previously mentioned shortcomings but its use is not widely spread in the analysis of empirical data in the evolutionary computing community. This paper provides three main contributions. First, we motivate the need for utilizing Bayesian data analysis and provide an overview of this topic. Second, we discuss the practical aspects of BDA to ensure that our models are valid and the results transparent. Finally, we provide five statistical models that can be used to answer multiple research questions. The online appendix provides a step-by-step guide on how to perform the analysis of the models discussed in this paper, including the code for the statistical models, the data transformations and the discussed tables and figures.Comment: In submissio