Prescriptive Analytics Data Canvas: Strategic Planning For Prescriptive Analytics In Smart Factories

Prescriptive Analytics deals with the task of prescribing actionable decisions. These decision-making processes are usually based on expert knowledge and existing Data Analytics solutions in a specific manufacturing environment. Prescriptive Analytics is seen as the next big thing for smart factories. Most Use Cases still focus on the descriptive, diagnostic, or predictive level. This is partly due to the complexity of Prescriptive Analytics algorithms. Another major challenge for practitioners is the lack of transparency when planning Use Cases as they are mostly seen as standalone initiatives. This paper presents a novel approach to developing Use Cases based on a fit-gap analysis for existing data objects for Prescriptive Production Management in the Smart Factory. First, a Prescriptive Analytics Data Canvas is developed to structure the input data for Prescriptive Analytics in the Smart Factory. Then, promising Use Cases are selected based on the Data Canvas and existing data collection methods. Synergies between different Use Cases in the same factory are derived. We demonstrate the functionality and usability of the developed artifact and method in a real-world IoT-Factory scenario

A Prescriptive Maintenance Aligned Production Planning and Control Reference Process

Digital innovations can improve various business processes, such as production planning and control (PPC). In the last years, prescriptive maintenance (PxM) emerged as a strategy to increase overall production performance, but an alignment of the PPC process with PxM has not been examined yet. To tackle this problem, a PxM-aligned PPC process is designed and evaluated in this study using a reference model development methodology, including a narrative literature review, a multivocal literature review, and eight expert interviews. The reference model shows where process elements benefit from PxM alignment, how alignment can be achieved from a process and output, data, function, and organization view, and where fits and gaps between theory and practice are

JIDOKA. Integration of Human and AI within Industry 4.0 Cyber Physical Manufacturing Systems

This book is about JIDOKA, a Japanese management technique coined by Toyota that consists of imbuing machines with human intelligence. The purpose of this compilation of research articles is to show industrial leaders innovative cases of digitization of value creation processes that have allowed them to improve their performance in a sustainable way. This book shows several applications of JIDOKA in the quest towards an integration of human and AI within Industry 4.0 Cyber Physical Manufacturing Systems. From the use of artificial intelligence to advanced mathematical models or quantum computing, all paths are valid to advance in the process of human–machine integration

Advances in Production Management Systems: Issues, Trends, and Vision Towards 2030

Since its inception in 1978, the IFIP Working Group (WG) 5.7 on Advances in Production Management Systems (APMS) has played an active role in the fields of production and production management. The Working Group has focused on the conception, development, strategies, frameworks, architectures, processes, methods, and tools needed for the advancement of both fields. The associated standards created by the IFIP WG5.7 have always been impacted by the latest developments of scientific rigour, academic research, and industrial practices. The most recent of those developments involves the Fourth Industrial Revolution, which is having remarkable (r)evolutionary and disruptive changes in both the fields and the standards. These changes are triggered by the fusion of advanced operational and informational technologies, innovative operating and business models, as well as social and environmental pressures for more sustainable production systems. This chapter reviews past, current, and future issues and trends to establish a coherent vision and research agenda for the IFIP WG5.7 and its international community. The chapter covers a wide range of production aspects and resources required to design, engineer, and manage the next generation of sustainable and smart production systems.acceptedVersio

From supply chains to demand networks. Agents in retailing: the electrical bazaar

A paradigm shift is taking place in logistics. The focus is changing from operational effectiveness to adaptation. Supply Chains will develop into networks that will adapt to consumer demand in almost real time. Time to market, capacity of adaptation and enrichment of customer experience seem to be the key elements of this new paradigm. In this environment emerging technologies like RFID (Radio Frequency ID), Intelligent Products and the Internet, are triggering a reconsideration of methods, procedures and goals. We present a Multiagent System framework specialized in retail that addresses these changes with the use of rational agents and takes advantages of the new market opportunities. Like in an old bazaar, agents able to learn, cooperate, take advantage of gossip and distinguish between collaborators and competitors, have the ability to adapt, learn and react to a changing environment better than any other structure. Keywords: Supply Chains, Distributed Artificial Intelligence, Multiagent System.Postprint (published version

Activating supply chain business models' value potentials through Systems Engineering

New business opportunities, driven by smart digitalization technology and initiatives such as Industry 4.0, significantly change business models and their innovation rate. The complexity of methodologies developed in recent decades for balancing exploration and exploitation activities of digital transformation has risen. Still, the desired integration levels across organizational levels were often not reached. Systems thinking promises to holistically consider interdisciplinary relationships and objectives of various stakeholders across supply chain ecosystems. Systems theory-based concepts can simultaneously improve value identification and aligned transformation among supply networks' organizational and technical domains. Hence, the study proposes synthesizing management science concepts such as strategic alignment with enterprise architecture concepts and artificial intelligence (AI)-driven business process optimization to increase innovation productivity and master the increasing rate of business dynamics at the same time. Based on a critical review, the study explores concepts for innovation, transformation, and alignment in the context of Industry 4.0. The essence has been compiled into a systems engineering-driven framework for agile value generation on operational processes and high-order capability levels. The approach improves visibility for orchestrating sustainable value flows and transformation activities by considering the ambidexterity of exploring and exploiting activities and the viability of supply chain systems and sub-systems. Finally, the study demonstrates the need to harmonize these concepts into a concise methodology and taxonomy for digital supply chain engineering.OA-hybri

Evolution of a Lean Smart Maintenance Maturity Model towards the new Age of Industry 4.0

Over the last few years, the complexity of asset and maintenance management of industrial plants and machinery in the producing industry has risen due to higher competition and volatile environments. Smart factories, Internet of Things (IoT) and the underlying digitisation of a significant number of processes are changing the way we have to think and work in terms of asset management. Existing Lean Smart Maintenance (LSM) philosophy, which focuses on the cost-efficient (lean) and the learning organisation (smart) perspectives enables a value-oriented, dynamic, and smart maintenance/asset management. The associated LSM maturity model is the evaluation tool that contains the normative, strategic, and operational aspects of industrial asset management, based on which numerous reorganisation projects have already been carried out in industrial companies. However, due to the ever-increasing development of Industry 4.0 (I4.0), it is necessary to extend the model by selected aspects of digitisation and digitalisation. Based on a structured literature review (SLR) of state of the art I4.0 maturity models, we were able to investigate the essential maturity items for I4.0. To restructure and expand the existing LSM maturity model, the principle of design science research (DSR) was used. The architecture of the LSM maturity model was based on the structure of the Capability Maturity Model Integration (CMMI). Further development of a Lean Smart Maintenance maturity model thus covers the future requirements of I4.0 and data science. It was possible to enhance existing categories with new artefacts from the I4.0 range to represent the influence of cyber-physical systems (CPS), (big) data and information management, condition monitoring (CM) and more. Furthermore, the originally defined LSM-Model was restructured for a more simplified application in industrial use cases

Smart city initiatives in the context of digital transformation – Scope, services and technologies

Digital transformation is an emerging trend in developing the way how the work is being done, and it is present in the private and public sector, in all industries and fields of work. Smart cities, as one of the concepts related to digital transformation, is usually seen as a matter of local governments, as it is their responsibility to ensure a better quality of life for the citizens. Some cities have already taken advantages of possibilities offered by the concept of smart cities, creating new values to all stakeholders interacting in the living city ecosystems, thus serving as examples of good practice, while others are still developing and growing on their intentions to become smart. This paper provides a structured literature analysis and investigates key scope, services and technologies related to smart cities and digital transformation as concepts of empowering social and collaboration interactions, in order to identify leading factors in most smart city initiatives