Approach to identify product and process state drivers in manufacturing systems using supervised machine learning

The developed concept allows identifying relevant state drivers of complex, multi-stage manufacturing systems holistically. It is able to utilize complex, diverse and high-dimensional data sets which often occur in manufacturing applications and integrate the important process intra- and inter-relations. The evaluation was conducted by using three different scenarios from distinctive manufacturing domains (aviation, chemical and semiconductor). The evaluation confirmed that it is possible to incorporate implicit process intra- and inter-relations on process as well as programme level through applying SVM based feature ranking. The analysis outcome presents a direct benefit for practitioners in form of the most important process parameters and state characteristics, so-called state drivers, of a manufacturing system. Given the increasing availability of data and information, this selection support can be directly utilized in, e.g., quality monitoring and advanced process control

Cascade Use and the Management of Product Lifecycles

This paper explores the challenges related to the End-Of-Life phase of products and circular systems of reuse and recycling within the commonly established frameworks of product lifecycles. Typically, Original Equipment Manufacturer-centric supply chain perspectives neglect the complexity at the End-Of-Life where many third-parties are involved in reuse and recycling activities. Based on a review of product lifecycle and related recycling literature, this study proposes the application of ‘cascades’, a term originally coined within the biomass domain. We propose and subsequently apply the ‘cascade use methodology’ and identify additional and value-adding End-Of-Life solutions for products and materials. The adoption of cascade utilization into product lifecycles is analyzed and critically discussed using case studies from independent remanufacturing and tire recycling, focusing on the End-Of-Life while excluding business models as renting or sharing. Although theoretically feasible, we argue that the practical adoption of ‘cascade use’ deserves more attention from researchers and practitioners in order to become an integral part of the comprehensive management of product lifecycles

Changing States of Multistage Process Chains

Generally, a process describes a change of state of some kind (state transformation). This state change occurs from an initial state to a concluding state. Here, the authors take a step back and take a holistic look at generic processes and process sequences from a state perspective. The novel perspective this concept introduces is that the processes and their parameters are not the priority; they are rather included in the analysis by implication. A supervised machine learning based feature ranking method is used to identify and rank relevant state characteristics and thereby the processes’ inter- and intrarelationships. This is elaborated with simplified examples of possible applications from different domains to make the theoretical concept and results more feasible for readers from varying domains. The presented concept allows for a holistic description and analysis of complex, multistage processes sequences. This stands especially true for process chains where interrelations between processes and states, processes and processes, or states and states are not fully understood, thus where there is a lack of knowledge regarding causations, in dynamic, complex, and high-dimensional environments

Data-driven Context Awareness of Smart Products in Discrete Smart Manufacturing Systems

Abstract Traditionally, smart-connected products are predominantly utilized during the usage phase of the product lifecycle. However, we argue that there are distinct benefits of system-integrated sensor systems during the beginning of life, more specifically in manufacturing and assembly. In this paper, we analyze the ability of a smart-connected product with an integrated sensor system to recognize and label different manufacturing processes, generating a distinct process fingerprint within a discrete smart manufacturing system. The ability of the smart-connected product to detect distinct manufacturing process patterns ('process fingerprint') enables the production planner and operator, e.g., to optimize the scheduling, improve part quality, and/or reduce the energy footprint. The experimental setup is based on a FestoDidactics CPlab with eight different manufacturing processes. The smart-connected product is equipped with a sensor system providing data from eight different sensors (e.g., temperature, humidity, acceleration). We used an Artificial Neural Network (ANN) algorithm to create a model to detect specific events/patterns within the dataset after labelling it manually over the course of a complete production cycle. The focal manufacturing process was the heating tunnel where the smart-connected product was exposed to a heat treatment process and sequence. The results of this prototypical implementation indicate that a smart-connected product can reliably recognize specific process patterns with a system-integrated sensor system during a simulated manufacturing process. While this work is only a first step, the potential applications and benefits are promising and further research should focus on the potential quality implications within smart manufacturing of product-integrated sensor readings compared to machine tool-based sensors, both of which monitored during the beginning of life. Smart products' integrated sensor systems provide the means to obtain measurements relevant for smart manufacturing systems that are not obtainable with common external sensor systems today

Smart Maintenance: a research agenda for industrial maintenance management

How do modernized maintenance operations, often referred to as “Smart Maintenance”, impact the performance of manufacturing plants? This question is a pressing challenge for practitioners and scholars in industrial maintenance management, in direct response to the transition to an industrial environment with pervasive digital technologies. This paper is the second part of a two-paper series. We present an empirically grounded research agenda that reflects the heterogeneity in industrial adoption and performance of Smart Maintenance. Focus groups and interviews with more than 110 experts from over 20 different firms were used to identify contingencies, responses, and performance implications of Smart Maintenance. The findings were transformed into a contingency model, providing the basis for a research agenda consisting of five principal areas: (1) environmental contingencies; (2) institutional isomorphism; (3) implementation issues related to change, investments and interfaces; (4) the four dimensions of Smart Maintenance; and (5) performance implications at the plant and firm level. The agenda can guide the field of industrial maintenance management to move from exploratory work to confirmatory work, studying the validity of the proposed concepts as well as the magnitude and direction of their relationships. This will ultimately help scholars and practitioners answer how Smart Maintenance can impact industrial performance

Smart Maintenance: an empirically grounded conceptualization

How do modernized maintenance operations, often referred to as “Smart Maintenance”, impact the performance of manufacturing plants? The inability to answer this question backed by data is a problem for industrial maintenance management, especially in light of the ongoing rapid transition towards an industrial environment with pervasive digital technologies. To this end, this paper, which is the first part of a two-paper series, aims to investigate and answer the question, “What is Smart Maintenance?”. The authors deployed an empirical, inductive research approach to conceptualize Smart Maintenance using focus groups and interviews with more than 110 experts from over 20 different firms. By viewing our original data through the lens of multiple general theories, our findings chart new directions for contemporary and future maintenance research. This paper describes empirical observations and theoretical interpretations cumulating in the first empirically grounded definition of Smart Maintenance and its four underlying dimensions; data-driven decision-making, human capital resource, internal integration, and external integration. In addition, the relationships between the underlying dimensions are specified and the concept structure formally modeled. This study thus achieves concept clarity with respect to Smart Maintenance, thereby making several theoretical and managerial contributions that guide both scholars and practitioners within the field of industrial maintenance management

Biologically Inspired Design of Context-Aware Smart Products

The rapid development of information and communication technologies (ICTs) and cyber–physical sys-tems (CPSs) has paved the way for the increasing popularity of smart products. Context-awareness isan important facet of product smartness. Unlike artifacts, various bio-systems are naturally characterizedby their extraordinary context-awareness. Biologically inspired design (BID) is one of the most commonlyemployed design strategies. However, few studies have examined the BID of context-aware smart prod-ucts to date. This paper presents a structured design framework to support the BID of context-awaresmart products. The meaning of context-awareness is defined from the perspective of product design.The framework is developed based on the theoretical foundations of the situated function–behavior–structure ontology. A structured design process is prescribed to leverage various biological inspirationsin order to support different conceptual design activities, such as problem formulation, structure refor-mulation, behavior reformulation, and function reformulation. Some existing design methods and emerg-ing design tools are incorporated into the framework. A case study is presented to showcase how thisframework can be followed to redesign a robot vacuum cleaner and make it more context-aware.Ó2019 THE AUTHORS. Published by Elsevier LTD on behalf of Chinese Academy of Engineering andHigher Education Press Limited Company. This is an open access article under the CC BY-NC-ND licens

Review of PPX Business Models : Adaptability and Feasibility of PPX Models in the Equipment Manufacturing Industry

The overall purpose of this study is to understand how manufacturing companies have so far made use of and can make use of pay-per-x (PPX) business models (BMs) largely in capital product markets, and which mechanisms have helped them in the implementation. Through systematic literature approach this study analysed 14 research publications which exclusively focused on PPX business models. The differences between PPX business model patterns were studied from three perspective, namely criticality of product, need of process knowledge and complexity of the process and its output. We find out that the pay-per-outcome business model, is more prevalent for products which are critical, needs extensive process knowledge and are rather complex. In contrarily, pay-per-output business model is more prevalent when these conditions are not met. However, none of these three factors prevents implementing other type of PPX business model but rather specific business model is more feasible when specific conditions are met. This paper contributes a much more in-depth qualitative view on the patterns and related qualitative arguments for the useful application of PPX models in equipment manufacturing industries and helps to understand the differences between PPX business model types.acceptedVersionPeer reviewe