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

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

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

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

Technological innovation for digital supply chains within small and medium size manufacturing enterprises

The rapidly growing world of digitalisation opens the doorway for the new era of automation that plays a crucial role within the industry. Furthermore, technological innovations that are emerging every day are disrupting traditional business processes especially within small and medium size manufacturing enterprises (SMEs). The current industrial revolution pioneer for profit maximisation with cost reduction shows a significant refinement in improving sustainability that drives forward digitalisation. Evidence shows that industries have identified digitalisation as a priority in the upcoming years as the global supply chain is equipping itself with the digital world in the current industrial revolution. Economic growth is dependent on SMEs around the world where small and medium size Manufacturing Enterprises (SMMEs) play a vital role in the current competitive world while they are not able to manage their supply chains effectively and efficiently due to a lack of optimisation of digitalisation. They identify that technological innovation is evident for transforming themselves with digital supply chain, while global market leading organisations are positioning themselves with the world of digitalisation to their end consumers in their supply chain utilising technological innovation virtually driving towards a new era of a digital ecosystem. This research aims to investigate the impact of technological innovation to foster and promote digital supply chain within SMMEs. Due to the exploratory nature, this study adopted a case study approach where the data is collected using a semi-structured interview across 4 cases from three various countries. The findings indicate a lack of framework for the digitalisation of supply chains within SMMEs, in addition to a lack of technological innovation and financial constraints that served as limiting factors for digitalisation of supply chain within organisations. Further, a framework has been developed consisting of five elements that have been identified from empirical data as being critical for Digital Supply Chain (DSC) transformation. The theoretical contributions of this research are the identification of problems faced, limitations of technological innovation, and an improved understanding of how digital supply chain transformation can be initiated and achieved in the context of SMMEs. The practical contribution of this study is imbedded in the developed framework in the form of recommended strategies for SMMEs for digitalisation of supply chain