Augmenting the Production Operators for Continuous Improvement

This paper discusses how continuous improvement activities can be supported by augmenting the operators in production. After a brief literature background, real life case examples from manufacturing companies are provided and discussed. Enabling technologies, specifically AR and embedded sensors, can guide the operators in execution of their tasks, quality verification of work done step by step, and data collection from both manual and automated operations in much higher levels of details. Collected data provides an empirical foundation for data-driven analysis and improvement potentials in production and quality operations. The paper contributes to theory and practice by providing research-based innovation experiences on this emerging topic of interest for manufacturing companies.acceptedVersio

Going one step further: towards cognitively enhanced problem-solving teaming agents

Operating current advanced production systems, including Cyber-Physical Systems, often requires profound programming skills and configuration knowledge, creating a disconnect between human cognition and system operations. To address this, we suggest developing cognitive algorithms that can simulate and anticipate teaming partners' cognitive processes, enhancing and smoothing collaboration in problem-solving processes. Our proposed solution entails creating a cognitive system that minimizes human cognitive load and stress by developing models reflecting humans individual problem-solving capabilities and potential cognitive states. Further, we aim to devise algorithms that simulate individual decision processes and virtual bargaining procedures that anticipate actions, adjusting the system’s behavior towards efficient goal-oriented outcomes. Future steps include the development of benchmark sets tailored for specific use cases and human-system interactions. We plan to refine and test algorithms for detecting and inferring cognitive states of human partners. This process requires incorporating theoretical approaches and adapting existing algorithms to simulate and predict human cognitive processes of problem-solving with regards to cognitive states. The objective is to develop cognitive and computational models that enable production systems to become equal team members alongside humans in diverse scenarios, paving the way for more efficient, effective goal-oriented solutions

Maturity level of predictive maintenance application in small and medium-sized industries: Case of Morocco

In order to remain competitive in the long term and to push the company's efficiency to its limits, entrepreneurs are more and more open to the idea of integrating into Industry 4.0 aiming mainly at filling the important downtimes and the associated productivity losses by implementing predictive maintenance. This concept, common in developed countries, is much less widespread in Morocco and even less in small and medium-sized Moroccan companies. The objective of this article is to study the maturity level of predictive maintenance in Moroccan small and medium-sized enterprises, through a questionnaire validated by experts and made available to several companies. Valid data from 115 companies throughout the kingdom operating in different sectors were collected and processed by descriptive and factorial analysis under SPSS software. The results obtained show that only 33% of our sample were able to implement predictive maintenance, and that the expected benefits of this approach are the minimization of downtime at 96.5% and the increase in productivity at 94.8%, The main challenges observed are the lack of team motivation and a corporate culture unsuited to digitalization, which represents 42.277% of the total variance, lack of financial resources at 12.916% of the total variance and lack of data protection at 11.644% of the total variance. This analysis indicates that the level of maturity regarding the application of predictive maintenance in Moroccan small and medium-sized companies is low, these rates can be used to improve the root causes

Towards a Framework for Smart Manufacturing adoption in Small and Medium-sized Enterprises

Smart Manufacturing (SM) paradigm adoption can scale production with demand without compromising on the time for order fulfillment. A smart manufacturing system (SMS) is vertically and horizontally connected, and thus it can minimize the chances of miscommunication. Employees in an SME are aware of the operational requirements and their responsibilities. The machine schedules are prepared based on the tasks a machine must perform. Predictive maintenance reduces the downtime of machines. Design software optimizes the product design. Production feasibility is checked with the help of simulation. The concepts of product life cycle management are considered for waste reduction. Employee safety, and ergonomics, identifying new business opportunities and markets, focus on employee education and skill enhancement are some of the other advantages of SM paradigm adoption. This dissertation develops an SM paradigm adoption framework for manufacturing SMEs by employing the instrumental research approach. The first step in the framework identified the technical aspects of SM, and this step was followed by identifying the research gaps in the suggested methods (in literature) and managerial aspects for adopting SM paradigm. The technical and the managerial aspects were integrated into a toolkit for manufacturing SMEs. This toolkit contains seven modular toolboxes that can be installed in five levels, depending on an SME’s readiness towards SM. The framework proposed in this dissertation focuses on how an SME’s readiness can be assessed and based on its present readiness what tools and practices the SMEs need to have to realize their tailored vision of SM. The framework was validated with the help of two SMEs cases that have recently adopted SM practices

Adopción de tecnologías de Inteligencia Artificial : un estudio para las empresas en Colombia

Este documento proporciona una descripción completa de la adopción de tecnologías de Inteligencia Artificial (IA) por parte de las empresas colombianas discriminando el análisis por sectores económicos al aprovechar un nuevo módulo presentado en la Encuesta Pulso Empresarial (EPE) del Departamento Administrativo Nacional de Estadística (DANE) en 2022. El módulo recopila datos de más de 8500 empresas sobre la adopción, razones de uso y no uso de tecnologías de IA. Se encuentra que la organización de procesos administrativos es la principal motivación para adoptar IA mientras que los costos de adquisición y la falta de experiencia para el uso son las barreras más importantes implementar tecnologías de IA. Mediante un modelo de probabilidad se evidencia que, entre otros factores, usar internet, plataformas digitales, hacer inversión en equipos de software, realizar actividades de investigación y desarrollo, incrementan la probabilidad de adoptar dichas tecnologías. En la investigación se añade como elemento innovador un conjunto de variables sobre percepciones y expectativas que tienen las empresas en cuanto a la situación económica presente y futura y su influencia en la adopción de tecnologías de IA.This document provides a broad description regarding the adoption of Artificial Intelligence (AI) technologies in Colombian companies. We managed to discriminate the analysis by economic sectors using a new module presented in the Encuesta Pulso Empresarial (EPE) of the National Administrative Department of Statistics (DANE) in 2022. The module collects data from more than 8,500 companies on the adoption, reasons for use and non-use of AI technologies. During the investigation, we discovered that organizing administrative processes is one of the main issues that the implementation of AI would solve. Nonetheless, high acquisition costs and lack of experienced personnel capable of using AI makes its implementation more difficult. Through a probability model, it is evident that, among other factors, using the Internet, digital platforms, investing in software equipment, carrying out research and development activities, increase the probability of adopting AI technologies. These research adds as an innovative element a set of variables considering perceptions and expectations that companies have related to present and future economic situations and its influence on the adoption of AI technologies

Movement Analytics: Current Status, Application to Manufacturing, and Future Prospects from an AI Perspective

Data-driven decision making is becoming an integral part of manufacturing companies. Data is collected and commonly used to improve efficiency and produce high quality items for the customers. IoT-based and other forms of object tracking are an emerging tool for collecting movement data of objects/entities (e.g. human workers, moving vehicles, trolleys etc.) over space and time. Movement data can provide valuable insights like process bottlenecks, resource utilization, effective working time etc. that can be used for decision making and improving efficiency. Turning movement data into valuable information for industrial management and decision making requires analysis methods. We refer to this process as movement analytics. The purpose of this document is to review the current state of work for movement analytics both in manufacturing and more broadly. We survey relevant work from both a theoretical perspective and an application perspective. From the theoretical perspective, we put an emphasis on useful methods from two research areas: machine learning, and logic-based knowledge representation. We also review their combinations in view of movement analytics, and we discuss promising areas for future development and application. Furthermore, we touch on constraint optimization. From an application perspective, we review applications of these methods to movement analytics in a general sense and across various industries. We also describe currently available commercial off-the-shelf products for tracking in manufacturing, and we overview main concepts of digital twins and their applications

Advances in Production Management Systems. Smart Manufacturing for Industry 4.0: IFIP WG 5.7 International Conference, APMS 2018, Seoul, Korea, August 26-30, 2018, Proceedings, Part II

International audienceBook Front Matter of AICT 53

Improving Demand Forecasting: The Challenge of Forecasting Studies Comparability and a Novel Approach to Hierarchical Time Series Forecasting

Bedarfsprognosen sind in der Wirtschaft unerlässlich. Anhand des erwarteten Kundenbe-darfs bestimmen Firmen beispielsweise welche Produkte sie entwickeln, wie viele Fabri-ken sie bauen, wie viel Personal eingestellt wird oder wie viel Rohmaterial geordert wer-den muss. Fehleinschätzungen bei Bedarfsprognosen können schwerwiegende Auswir-kungen haben, zu Fehlentscheidungen führen, und im schlimmsten Fall den Bankrott einer Firma herbeiführen. Doch in vielen Fällen ist es komplex, den tatsächlichen Bedarf in der Zukunft zu antizipie-ren. Die Einflussfaktoren können vielfältig sein, beispielsweise makroökonomische Ent-wicklung, das Verhalten von Wettbewerbern oder technologische Entwicklungen. Selbst wenn alle Einflussfaktoren bekannt sind, sind die Zusammenhänge und Wechselwirkun-gen häufig nur schwer zu quantifizieren. Diese Dissertation trägt dazu bei, die Genauigkeit von Bedarfsprognosen zu verbessern. Im ersten Teil der Arbeit wird im Rahmen einer überfassenden Übersicht über das gesamte Spektrum der Anwendungsfelder von Bedarfsprognosen ein neuartiger Ansatz eingeführt, wie Studien zu Bedarfsprognosen systematisch verglichen werden können und am Bei-spiel von 116 aktuellen Studien angewandt. Die Vergleichbarkeit von Studien zu verbes-sern ist ein wesentlicher Beitrag zur aktuellen Forschung. Denn anders als bspw. in der Medizinforschung, gibt es für Bedarfsprognosen keine wesentlichen vergleichenden quan-titativen Meta-Studien. Der Grund dafür ist, dass empirische Studien für Bedarfsprognosen keine vereinheitlichte Beschreibung nutzen, um ihre Daten, Verfahren und Ergebnisse zu beschreiben. Wenn Studien hingegen durch systematische Beschreibung direkt miteinan-der verglichen werden können, ermöglicht das anderen Forschern besser zu analysieren, wie sich Variationen in Ansätzen auf die Prognosegüte auswirken – ohne die aufwändige Notwendigkeit, empirische Experimente erneut durchzuführen, die bereits in Studien beschrieben wurden. Diese Arbeit führt erstmals eine solche Systematik zur Beschreibung ein. Der weitere Teil dieser Arbeit behandelt Prognoseverfahren für intermittierende Zeitreihen, also Zeitreihen mit wesentlichem Anteil von Bedarfen gleich Null. Diese Art der Zeitreihen erfüllen die Anforderungen an Stetigkeit der meisten Prognoseverfahren nicht, weshalb gängige Verfahren häufig ungenügende Prognosegüte erreichen. Gleichwohl ist die Rele-vanz intermittierender Zeitreihen hoch – insbesondere Ersatzteile weisen dieses Bedarfs-muster typischerweise auf. Zunächst zeigt diese Arbeit in drei Studien auf, dass auch die getesteten Stand-der-Technik Machine Learning Ansätze bei einigen bekannten Datensät-zen keine generelle Verbesserung herbeiführen. Als wesentlichen Beitrag zur Forschung zeigt diese Arbeit im Weiteren ein neuartiges Verfahren auf: Der Similarity-based Time Series Forecasting (STSF) Ansatz nutzt ein Aggregation-Disaggregationsverfahren basie-rend auf einer selbst erzeugten Hierarchie statistischer Eigenschaften der Zeitreihen. In Zusammenhang mit dem STSF Ansatz können alle verfügbaren Prognosealgorithmen eingesetzt werden – durch die Aggregation wird die Stetigkeitsbedingung erfüllt. In Expe-rimenten an insgesamt sieben öffentlich bekannten Datensätzen und einem proprietären Datensatz zeigt die Arbeit auf, dass die Prognosegüte (gemessen anhand des Root Mean Square Error RMSE) statistisch signifikant um 1-5% im Schnitt gegenüber dem gleichen Verfahren ohne Einsatz von STSF verbessert werden kann. Somit führt das Verfahren eine wesentliche Verbesserung der Prognosegüte herbei. Zusammengefasst trägt diese Dissertation zum aktuellen Stand der Forschung durch die zuvor genannten Verfahren wesentlich bei. Das vorgeschlagene Verfahren zur Standardi-sierung empirischer Studien beschleunigt den Fortschritt der Forschung, da sie verglei-chende Studien ermöglicht. Und mit dem STSF Verfahren steht ein Ansatz bereit, der zuverlässig die Prognosegüte verbessert, und dabei flexibel mit verschiedenen Arten von Prognosealgorithmen einsetzbar ist. Nach dem Erkenntnisstand der umfassenden Literatur-recherche sind keine vergleichbaren Ansätze bislang beschrieben worden

The impact of crisis management on supply chain management to improve the performance continuity in the security sector in Abu Dhabi in the UAE

The United Arab Emirates is one of the most attractive countries for tourism and investment, it places additional strains on the country's public security sector in order to maintain the highest levels of safety and security., especially in the capital of the UAE, Abu Dhabi. It is known that effective crisis management contributes significantly to supply chains to ensure the continuity of civil activities in the country. Therefore, this study aims to explore the impact of effective crisis management functions on supply chain management in order to ensure business continuity in the security sector in Abu Dhabi. The study adopted leadership theory and communication theory in situational crises. The proposed conceptual framework explains the relationship between the practices of crisis management, supply chain management, and performance continuity. The research design in this study is scientific the research in this study is scientific, deductive, exploratory, and quantitative. This study employed questionnaires like those employed in the previous studies., which were adopted from previous studies. The population of this study is all employees who work in fields related to national security and their number is estimated to be at least 10,000 civil and military employees the target sample is 366 employees. As for the predictive model of performance continuity, the results show that the sub-model has moderate statistical significance as it can predict 49.8% of the variance in the continuity of performance based on the three predictors and supply chain management. The study showed that supply chain management plays an important mediating role, and the arrangement of the three independent variables is public relations, followed by the crisis management team, and then the crisis management strategy

Smart Service Innovation: Organization, Design, and Assessment

Background: The emergence of technologies such as the Internet of Things, big data, cloud computing, and wireless communication drives the digital transformation of the entire society. Organizations can exploit these potentials by offering new data-driven services with innovative value propositions, such as carsharing, remote equipment maintenance, and energy management services. These services result from value co-creation enabled by smart service systems, which are configurations of people, processes, and digital technologies. However, developing such systems was found to be challenging in practice. This is mainly due to the difficulties of managing complexity and uncertainty in the innovation process, as contributions of various actors from multiple disciplines must be coordinated. Previous research in service innovation and service systems engineering (SSE) has not shed sufficient light on the specifics of smart services, while research on smart service systems lacks empirical grounding. Purpose: This thesis aims to advance the understanding of the systematic development of smart services in multi-actor settings by investigating how smart service innovation (SSI) is conducted in practice, particularly regarding the participating actors, roles they assume, and methods they apply for designing smart service systems. Furthermore, the existing set of methods is extended by new methods for the design-integrated assessment of smart services and service business models. Approach: Empirical and design science methods were combined to address the research questions. To explore how SSI is conducted in practice, 25 interviews with experts from 13 organizations were conducted in two rounds. Building on service-dominant logic (SDL) as a theoretical foundation and a multi-level framework for SSI, the involvement of actors, their activities, employed means, and experienced challenges were collected. Additionally, a case study was used to evaluate the suitability of the Lifecycle Modelling Language to describe smart service systems. Design science methods were applied to determine a useful combination of service design methods and to build meta-models and tools for assessing smart services. They were evaluated using experiments and the talk aloud method. Results: On the macro-level, service ecosystems consist of various actors that conduct service innovation through the reconfiguration of resources. Collaboration of these actors is facilitated on the meso-level within a project. The structure and dynamics of project configurations can be described through a set of roles, innovation patterns, and ecosystem states. Four main activities have been identified, which actors perform to reduce uncertainty in the project. To guide their work, actors apply a variety of means from different disciplines to develop and document work products. The approach of design-integrated business model assessment is enabled through a meta-model that links qualitative aspects of service architectures and business models with quantitative assessment information. The evaluation of two tool prototypes showed the feasibility and benefit of this approach. Originality / Value: The results reported in this thesis advance the understanding of smart service innovation. They contribute to evidence-based knowledge on service systems engineering and its embedding in service ecosystems. Specifically, the consideration of actors, roles, activities, and methods can enhance existing reference process models. Furthermore, the support of activities in such processes through suitable methods can stimulate discussions on how methods from different disciplines can be applied and combined for developing the various aspects of smart service systems. The underlying results help practitioners to better organize and conduct SSI projects. As potential roles in a service ecosystem depend on organizational capabilities, the presented results can support the analysis of ex¬ternal dependencies and develop strategies for building up internal competencies.:Abstract iii Content Overview iv List of Abbreviations viii List of Tables x List of Figures xii PART A - SYNOPSIS 1 1 Introduction 2 1.1 Motivation 2 1.2 Research Objectives and Research Questions 4 1.3 Thesis Structure 6 2 Research Background 7 2.1 Smart Service Systems 7 2.2 Service-Dominant Logic 8 2.3 Service Innovation in Ecosystems 11 2.4 Systematic Development of Smart Service Systems 13 3 Research Approach 21 3.1 Research Strategy 21 3.2 Applied Research Methods 22 4 Summary of Findings 26 4.1 Overview of Research Results 26 4.2 Organizational Setup of Multi-Actor Smart Service Innovation 27 4.3 Conducting Smart Service Innovation Projects 32 4.4 Approaches for the Design-integrated Assessment of Smart Services 39 5 Discussion 44 5.1 Contributions 44 5.2 Limitations 46 5.3 Managerial Implications 47 5.4 Directions for Future Research 48 6 Conclusion 54 References 55 PART B - PUBLICATIONS 68 7 It Takes More than Two to Tango: Identifying Roles and Patterns in Multi-Actor Smart Service Innovation 69 7.1 Introduction 69 7.2 Research Background 72 7.3 Methodology 76 7.4 Results 79 7.5 Discussion 90 7.6 Conclusions and Outlook 96 7.7 References 97 8 Iterative Uncertainty Reduction in Multi-Actor Smart Service Innovation 100 8.1 Introduction 100 8.2 Research Background 103 8.3 Research Approach 109 8.4 Findings 113 8.5 Discussion 127 8.6 Conclusions and Outlook 131 8.7 References 133 9 How to Tame the Tiger – Exploring the Means, Ends, and Challenges in Smart Service Systems Engineering 139 9.1 Introduction 139 9.2 Research Background 140 9.3 Methodology 143 9.4 Results 145 9.5 Discussion and Conclusions 151 9.6 References 153 10 Combining Methods for the Design of Digital Services in Practice: Experiences from a Predictive Costing Service 156 10.1 Introduction 156 10.2 Conceptual Foundation 157 10.3 Preparing the Action Design Research Project 158 10.4 Application and Evaluation of Methods 160 10.5 Discussion and Formalization of Learning 167 10.6 Conclusion 169 10.7 References 170 11 Modelling of a Smart Service for Consumables Replenishment: A Life Cycle Perspective 171 11.1 Introduction 171 11.2 Life Cycles of Smart Services 173 11.3 Case Study 178 11.4 Discussion of the Modelling Approach 185 11.5 Conclusion and Outlook 187 11.6 References 188 12 Design-integrated Financial Assessment of Smart Services 192 12.1 Introduction 192 12.2 Problem Analysis 195 12.3 Meta-Model Design 200 12.4 Application of the Meta-Model in a Tool Prototype 204 12.5 Evaluation 206 12.6 Discussion 208 12.7 Conclusions 209 12.8 References 211 13 Towards a Cost-Benefit-Analysis of Data-Driven Business Models 215 13.1 Introduction 215 13.2 Conceptual Foundation 216 13.3 Methodology 218 13.4 Case Analysis 220 13.5 A Cost-Benefit-Analysis Model for DDBM 222 13.6 Conclusion and Outlook 225 13.7 References 226 14 Enabling Design-integrated Assessment of Service Business Models Through Factor Refinement 228 14.1 Introduction 228 14.2 Related Work 229 14.3 Research Goal and Method 230 14.4 Solution Design 231 14.5 Demonstration 234 14.6 Discussion 235 14.7 Conclusion 236 14.8 References 23