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While there has been increased digitization of private homes, only little has been done to understand these specific home technologies, how they serve consumers, among other issues. “Smart home technology” (SHT) refer to a wide range of artifacts from cleaning aids to energy advisors. Given this breadth, clarity surrounding the key characteristics and the multi-faceted impact of SHT is needed to conduct more directed research on SHT. We propose a taxonomy to help outline the salient intended outcomes of SHT. Through a process involving five iterations, we analyzed and classified 79 technologies (gathered from literature and industry reports). This uncovered seven dimensions encompassing 20 salient characteristics. We believe these dimensions/characteristics will help researchers and organizations better design and study the impacts of these technologies. Our long-term agenda is to use the proposed taxonomy for an exploratory inquiry to understand tensions occurring when personal and sustainability-related outcomes compete.
In times of climate change and growing urbanization, the way food is produced and consumed also changes. Meanwhile, digitization is transforming farming practices, which also applies to the domestic growing of crops. More and more so-called smart home farms (SHF) are finding their way into private households. This paper conceptualizes the unique nature of enabled smart services and their underlying technology. Following an inductive interpretive approach, this study explores the antecedents of smart home farming practices. Our sample consists of eleven actual smart home farmers. We found six constructs to be of salient importance: expected outcomes related to harvesting, positive feelings, and sustainability; a combination of one's affinity for green and novel technologies; and the smartness and visibility of the enabled services. In the outlook, we present some preliminary thoughts for testing our qualitative findings.
Facing ever-looming climate change, studying the drivers for individuals' Information Systems (IS) Use to reduce environmental harm gains momentum. While extant research on the antecedents of sustainable IS Use has focused on specific theories, interventions, contexts, and technologies, a holistic understanding has become increasingly elusive, with a synthesis remaining absent. We employ a systematic literature review methodology to shed light on the driving antecedents for sustainable IS Use among individual consumers. Our results build on findings of 29 empirical studies drawn from 598 articles retrieved from our premier outlets and a forward/backward search. The analysis reveals six salient complementary antecedents: Relief, Empowerment, Default, User-centricity, Salience, and Encouragement. We recommend considering these concepts when developing, deploying, promoting, or regulating digital technologies to mitigate individual consumers' emissions. Along with memorable and implementable concepts, our theoretical framework offers a novel conceptualization and four promising avenues for researchers on sustainable IS Use.
The proliferation of smart technologies transforms the way individual consumers perform tasks. Considerable research alludes that smart technologies are often related to domestic energy consumption. However, it remains unclear how such technologies transform tasks and thereby impact our planet. We explore the role of technological smartness in personal day-to-day tasks that help create a more sustainable future. In the absence of theory, but facing extensive changes in everyday life enabled by smart technologies, we draw on phenomenon-based theorizing (PBT) guidelines. As anchor, we refer to task endogeneity related to task-technology fit theory (TTF). As infusion, we employ theory on public goods. Our model proposes novel relations between the concepts of smart autonomy and -transparency with sustainable task outcomes, mediated by task convenience and task significance. We discuss some implications, limitations, and future research opportunities.
The early involvement of experiences gained through intelligence and data analysis is becoming increasingly important in order to develop new products, leading to a completely different conception of product creation, development and engineering processes using the advantages that the dedication of the digital twin entails. Introducing a novel stage gate process in order to be holistically anchored in learning factories adopting idea generation and idea screening in an early stage, beta testing of first prototypes, technical implementation in real production scenarios, business analysis, market evaluation, pricing, service models as well as innovative social media portals. Corresponding product modelling in the sense of sustainability, circular economy, and data analytics forecasts the product on the market both before and after market launch with the interlinking of data interpretation nearby in real-time. The digital twin represents the link between the digital model and the digital shadow. Additionally, the connection of the digital twin with the product provides constantly updated operating status and process data as well as mapping of technical properties and real-world behaviours. A future-networking product, by embedded information technology with the ability to initiate and carry out one's own further development, is able to interact with people and environments and thus is relevant to the way of life of future generations. In today's development work for this new product creation approach, on one hand, "Werk150" is the object of the development itself and on the other hand the validation environment. In the next step, new learning modules and scenarios for trainings at master level will be derived from these findings.
Changing requirements and qualification profiles of employees, increasingly complex digital systems up to artificial intelligence, missing standards for the seamless embedding of existing resources and unpredictable return on investments are just a few examples of the challenges of an SME in the age of digitalisation. In most cases there is a lack of suitable tools and methods to support companies in the digital transformation process in the value creation processes, but also of training and learning materials. A European research project (BITTMAS - Business Transformation towards Digitalisation and Smart systems, ERASMUS+, 2016-1 DE02-KA202-003437) with international partners from science, associations and industry has addressed this issue and developed various methods and instruments to support SMEs. Within the scope of a literature search, 16 suitable digitalisation concepts for production and logistics were identified. In the following, a learning platform with a literature database with multivariable sorting options according to branches and keywords of digitalisation, a video gallery with basic and advanced knowledge and a glossary were created in order to provide the user with consolidated and structured specialist knowledge. The 16 identifying concepts for transforming value-added processes in the context of digitalisation were transferred to a learning platform using developed learning paths in coaching and training to online course modules including test questions. A maturity model was developed and implemented in a self assessment tool for the analysis to identify the potential of digitalisation in production and logistics in relation to the current technological digitalisation level of the company. As a result, the user receives one or more of the 16 potential digitalisation concepts suggested or the delta for the necessary, not yet available enabler technologies is presented as a spider diagram. For a successful implementation of the identified suitable digitalisation concepts in production and logistics, a further tool was developed to identify supplementary requirements for all company divisions and stakeholders in relation to the "digital transformation" in the form of a self-evaluation. This paper presents the methods and tools developed, the accompanying learning materials and the learning platform.
Forecasting demand is challenging. Various products exhibit different demand patterns. While demand may be constant and regular for one product, it may be sporadic for another, as well as when demand occurs, it may fluctuate significantly. Forecasting errors are costly and result in obsolete inventory or unsatisfied demand. Methods from statistics, machine learning, and deep learning have been used to predict such demand patterns. Nevertheless, it is not clear for what demand pattern, which algorithm would achieve the best forecast. Therefore, even today a large number of models are used to forecast on a test period. The model with the best result on the test period is used for the actual forecast. This approach is computationally and time intensive and, in most cases, uneconomical. In our paper we show the possibility to use a machine learning classification algorithm, which predicts the best possible model based on the characteristics of a time series. The approach was developed and evaluated on a dataset from a B2B-technical-retailer. The machine learning classification algorithm achieves a mean ROC-AUC of 89%, which emphasizes the skill of the model.
Machine learning (ML) techniques are rapidly evolving, both in academia and practice. However, enterprises show different maturity levels in successfully implementing ML techniques. Thus, we review the state of adoption of ML in enterprises. We find that ML technologies are being increasingly adopted in enterprises, but that small and medium-size enterprises (SME) are struggling with the introduction in comparison to larger enterprises. In order to identify enablers and success factors we conduct a qualitative empirical study with 18 companies in different industries. The results show that especially SME fail to apply ML technologies due to insufficient ML knowhow. However, partners and appropriate tools can compensate this lack of resources. We discuss approaches to bridge the gap for SME.
Im Rahmen der wissenschaftlichen Vertiefung an der Hochschule Reutlingen befasst sich diese Arbeit mit der Untersuchung der Anforderungen und der Machbarkeit zur computergestützten Erkennung der Deutschen Gebärdensprache (DGS) und des deutschen Fingeralphabets. Die Erkenntnisse aus dieser Arbeit dienen als Grundlage zur Entwicklung eines Systems zur Übersetzung von Gebärden der DGS oder des Fingeralphabets in die deutsche Schriftsprache. Zunächst werden grundlegende Informationen zu Geschichte, Aufbau und Grammatik der DGS und des Fingeralphabets aufgeführt. Die Erkennung der Gebärden soll durch optische Bewegungssensoren erfolgen. Hierfür werden unterschieliche Sensortypen betrachtet und verglichen. Im weiteren Verlauf erfolgt die Analyse der benutzerspezifischen und technischen Anforderungen. Erstere basieren auf der Befragung einer Fokusgruppe aus gehörlosen und hörenden Menschen aus dem Bereich der Gehörlosen-, Schwerhörigen- und Sprachbehindertenpädagogik. Abgeleitet aus den Informationen der Anforderungsanalyse ergibt sich, bis zu einem gewissen Grad, die Machbarkeit aus technischer und benutzerspezifischer Sicht. Abschließend erfolgen die Zusammenfassung der Anforderungen, welche an das zu entwickelnde System gestllt werden, sowie eine Handlungsempfehlung für die Entwicklung eines Prototyps.
Learning factories on demand
(2021)
Learning Factories are research and learning environments that demonstrate new concepts and technologies for the industry in a practical environment. The interaction between physical and virtual components is a central aspect. The mediation and presentation usually occur directly in the learning factory and are thus limited in time and concerning the user group. A learning factory- on-demand- can be provided by dividing and virtualizing the individual components via containers and microservices. This enables both local operation and operation hybrid cloud or cloud systems. Physical components can be mapped either through standardized interfaces or suitable emulators. Using the example of the Learning Factory at Reutlingen University (Werk150), it will be shown how different use cases can be made available utilizing software-based orchestration, thus promoting broader and more independent teaching.
The imparting of knowledge and skills in STEM education, especially under the influence of the Covid-19 pandemic, is increasingly taking place online and through digital formats. The partially asynchronous instruction eliminates, on the one hand, the social relation in the learning process and, on the other hand, the direct experience with physical objects. Here, the digital learning systems provide learning tools and controls to support the learning process on a general basis. Existing methods for simulating physical objects (digital twins) are also used to a minimal extent. The following approach presents a learning system framework that enables individualized learning, including all dimensions (social, physical). Implementing a concept that uses a personalized assistance system to orchestrate the individual learning steps enables efficient and effective learning. Applying the learning system framework exemplifies the STEM education at Reutlingen University in the logistics learning factory Werk150.
Prior to the introduction of AI-based forecast models in the procurement department of an industrial retail company, we assessed the digital skills of the procurement employees and surveyed their attitudes toward a new digital technology. The aim of the survey was to ascertain important contextual factors which are likely to influence the acceptance and the successful use of the new forecast tool. What we find is that the digital skills of the employees show an intermediate level and that their attitudes toward key aspects of new digital technologies are largely positive. Thus, the conditions for high acceptance and the successful use of the models are good, as evidenced by the high intention of the procurement staff to use the models. In line with previous research, we find that the perceived usefulness of a new technology and the perceived ease of use are significant drivers of the willingness to use the new forecast tool.
The high system flexibility necessary for the full automation of complex and unstructured tasks leads to increased complexity, thus higher costs. On the other hand, the effectiveness and performance of such systems decrease, explaining the unfulfilled potential of robotcs in sectors such as intralogistics, where the benefits of a robotic solution rarely justify its costs. Taking the distance from the false idea that a task should be either fully automated, or fully manual, this aper presents a method for design of a lean human-robot interaction (HRI) withe the objective of the "right level of automation", where functions are divided among human and automated agends, so that the overall process gains in performances and/or costs. ... The 10 progressive steps of the method are presented and discussed with reference to their graphical tool: the House of Quality Interaction.
Compared to the automotive sector, where automation is the rule, in many other less standardized sectors automation is still the exception. This could soon hurt the productivity of industrialized countries, where the unemployment is low and the population is aging. Phenomena like the recent downfall in productivity, due to lockdowns and social distancing for prevention of health hazards during the COVID19 pandemic, only add to the problem. For these reasons, the relevance, motivation and intention for more automation in less standardized sectors has probably never been higher. However, available statistics say that providers and users of technologies struggle to bring more automation into action in automation-unfriendly sectors. In this paper, we present a decision support method for investment in automation that tackles the problem: the STIC analysis. The method takes a holistic and quantitative approach tying together technological, context-related and economic input parameters and synthetizing them in a final economic indicator. Thanks to the modelling of such parameters, it is possible to gain sensibility on the technological and/or process adjustments that would have the highest impact on the efficiency of the automation, thereby delivering value for both technology users and technology providers.
In this paper it is first identified the trade-off among costs, flexibility and performances of autonomous robotic solutions for material handling processes, where adding value with automation is not as trivial as in production processes: hence the requirement for automated solutions to be simple, lean and efficient becomes even stricter. Then a method for modelling and comparing differential performances and costs of manual and autonomous solutions is developed. As a result of the method, a smart man-machine collaborative interface is designed and its impact evaluated on a specific case of study. Results are then generalized and prove the strong conclusions that in unconstrained environments, where full standardization cannot be achieved, the risk of investing in autonomous solutions can only be mitigated by creating a fast and smart man-machine collaborative interface.
Planning of available resources considering ergonomics under deterministic highly variable demand
(2020)
In this paper, a method for hybrid short- to long-term planning of available resources for operations is presented, which is based on a known or deterministically forecasted but highly variable demand. The method considers quantitative measures such as the performance and the availability of resources, ergonomically relevant KPI and ultimately process costs in order to serve as a pragmatic planning tool for operations managers in SMEs. Specifically, the method enables exploiting the ergonomic advantages of available flexible automation technology (e.g. AGVs or picking robots), while assuring that these do not represent a capacity bottleneck. After presenting the method along with the necessary assumptions, mainly concerning the availability of data for the calculations, we report a case study that quantifies the impact of throughput variability on the selection of different process alternatives, where different teams of resources are used.
The paper focuses on a recently introduced paradigm for the logistic process of picking, with respect to the man-to-goods and goods-to-man concept: the robot to-goods. First the task and system architecture of the fast deployable autonomous commissioning system are described, then the economic efficiency of the system is analysed in a real business case scenario using a simplified method, which is explained and discussed. The clearly positive net present value of the investment and the short payback period obtained in the business case prove how the robot-to-goods paradigm for the commissioning process, implemented through the automation of the forklift platform, is economically attractive for small and medium size enterprises.
In standardized sectors such as the automotive, the cost-benefit ratio of automation solutions is high as they contribute to increase capacity, decrease costs and improve product quality. In less standardized application fields, the contribution of automation to improvements in capacity, cost and quality blurs. The automation of complex and unstructured tasks requires sophisticated, expensive and low-performing systems, whose impact on product quality is oftentimes not directly perceived by customers. As a result, the full automation of process chains in the general manufacturing or the logistic sectors is often a sub optimal solution. Taking the distance from the false idea that a process should be either fully automated, or fully manual, this paper presents a novel heuristic method for design of lean human-robot interaction, the Quality Interaction Function Deployment, with the objective of the “right level of automation”. Functions are divided among human and automated agents and several automation scenarios are created and evaluated with respect to their compliance to the requirements of all process´ stakeholders. As a result, synergies among operators (manual tasks) and machines (automated tasks) are improved, thus reducing time-losses and increasing productivity.
Powered by e-commerce and vital in the manufacturing industry, intralogistics became an increasingly important and labour-intensive process. In highly standardized automation-friendly environments, such as the automotive sector, most of efficiently automatable intralogistics tasks have already been automated. Due to aging population in EU and ergonomic regulations, the urge to automate intralogistics tasks became consistent also where product and process standardization is lower. That is the case of the production line or cell material supply process, where an increasing number of product variants and individually customized products combined with the necessary ability of reacting to changes in market conditions led to smaller and more frequent replenishment to the points of use in the production plant and to the chaotic addition of production cells in shop floor layout. This led in turn to inevitable traffic growth with unforeseeable related delays and increased level of safety threats and accidents. In this paper, we use the structured approach of the Quality Interaction Function Deployment to analyse the process of supply of assembly lines, seeking the most efficient combination of automation and manual labour, satisfying all stakeholders´ requirements. Results are presented and discussed.
According to a recent survey the great majority of players in logistics are planning to adopt one or more robotic solutions until 2019. Technical solutions for automation of processes in logistics are often available as a market-ready product, but the lack of standardization and skepticism towards long term investments are often the reasons why these solutions are not implemented on a large scale. This paper is set to bridge the gap between the world of technologies and the one of applications in order to help investors, robot producers and system integrators to decide on which branch of logistics to set their focus. The three main branches Courier Express Parcel (CEP), contract logistics and production logistics are briefly defined and distinguished through their characteristic factors and parameters. Then a method based on the analysis of three parameters (operative costs, required performance and flexibility) in the three branches is set to identify the most convenient branch of logistics for investing in new technologies, namely the one in which the risk of investment is lower, the return is higher and faster. The conclusion of the method shows that higher labor costs, strict regulations and higher standardization make the production logistics the most suitable branch for investments in emerging automation solutions.
The high system flexibility necessary for the full automation of complex and unstructured tasks leads to increased technological complexity, thus to higher costs and lower performance. In this paper, after an introduction to the different dimensions of flexibility, a method for flexible modular configuration and evaluation of systems of systems is introduced. The method starts from process requirements and, considering factors such as feasibility, development costs, market potential and effective impact on the current processes, enables the evaluation of a flexible systems of systems equipped with the needed functionalities before its actual development. This allows setting the focus on those aspects of flexibility that add market value to the system, thus promoting the efficient development of systems addressed to interested customers in intralogistics. An example of application of the method is given and discussed.
Competing logics in evaluating employee performance : building compromises through conventions
(2015)
Current research argues that competing institutional logistics1 can co-exist enduringly and investigates how organizations cope with such institutional complexity (Greenwood et al. 2011). Thereby, the role of practices for handling competing logics has been overlooked and it is currently only to limited extent understood how organizations establish compromises between competing logics. Therefore, we investigated the recent performance appraisal reform of a German public sector organization that occurred in 2008 (see also Kozica, Brandl 2015). BAND (the pseudonym for our organization) has been using performance appraisals for several decades, and performance appraisals have already become entrenched instruments (Zeitz, Mittal, McAulay 1999) for handling staff promotion decisions. While BAND accepted the accountability logic of the performance appraisal, the professional logic (which is based on trust and comradeship as a high value of being professional in our organization) is accepted too and BAND has established a fine-grained compromise between the different logics. During the recent reform of the performance appraisal system, however, this compromise has broken up and challenged organizational members to (re-)arrange a compromise. By using French convention school of thinking (Boltanski, Thévenot 2006) we address how BAND copes with conflicting logics by forming compromises in organizational practices. Thereby, we show that the concept of convention is particularly promising for understanding of how organizations deal with institutional complexity. More broadly, our argument contributes to the elaboration of an organizational theory for the institutional logics discussion that explains how organizational and individual actions are interlinked.
Imagine a world in which the search for tomorrow's trends of (software) products is not subject to a long and laborious data search but is possible with a single mouse click. Through the use of artificial intelligence (AI), this reality is made possible and is to be further advanced through research. The study therefore aims to provide an initial overview of the young research field. Based on research, expert interviews, company and student surveys, current application possibilities of AI in the innovation process (defined as Smart Innovation), existing challenges that slow down the further development are discussed in more detail and future application possibilities are presented. Finally, a recommendation for action is made for business, politics and science to help overcome the current obstacles together and thus drive the future of Smart Innovation.
Teaching at assembly workstations in production in SMEs (small and medium sized companies) often does not take place at all or only insufficiently. In addition to the lack of technical content, there are also aggravatingly incorrect movement sequences from an ergonomic point of view, which "untrained" people usually automatically acquire. An AI based approach is used to analyze a definite workflow for a specific assembly scope regarding the behavior of several employees. Based on these different behaviors, the AI gives feedback at which points in time, work steps and movement’s particularly dangerous incorrect postures occur. Motion capturing and digital human model simulation in combination with the results of the AI define the optimized workflow. Individual employees can be trained directly due to the fact that AI identifies their most serious incorrect postures and provide them with a direct analogy of their “wrong” posture and “easy on the joints posture”. With the assistance of various test persons, the AI can conduct a study in which the most frequently occurring incorrect postures can be identified. This could be realized in general or tailored to specific groups of people (e.g. "People over 1.90m tall must be particularly careful not to make the following mistake...). The approach will be tested and validated at the Werk150, the factory of the ESB Business School, on the campus of the Reutlingen University. The new gained knowledge will be used subsequently for training in SMEs.
Framework for integrating intelligent product structures into a flexible manufacturing system
(2023)
Increasing individualisation of products with a high variety and shorter product lifecycles result in smaller lot sizes, increasing order numbers, and rising data and information processing for manufacturing companies. To cope with these trends, integrated management of the products and manufacturing information is necessary through a “product-driven” manufacturing system. Intelligent products that are integrated as an active element within the controlling and planning of the manufacturing process can represent flexibility advantages for the system. However, there are still challenges regarding system integration and evaluation of product intel-ligence structures. In light of these trends, this paper proposes a conceptual frame-work for defining, analysing, and evaluating intelligent products using the example of an assembly system. This paper begins with a classification of the existing problems in the assembly and a definition of the intelligence level. In contrast to previous approaches, the analysis of products is expanded to five dimensions. Based on this, a structured evaluation method for a use case is presented. The structure of solving the assembly problem is provided by the use case-specific ontology model. Results are presented in terms of an assignment of different application areas, linking the problem with the target intelligence class and, depending on the intelligence class of the product, suggesting requirements for implementation. The conceptual frame-work is evaluated by utilising a case study in a learning factory. Here, the model-mix assembly is controlled actively by the workpiece carrier in terms of transferring the variant-specific work instructions to the operator and the collaborative robot (cobot) at the workstations. The resulting system thus enables better exploitation of the poten-tials through less frequent errors and shorter search times. Such an implementation has demonstrated that the intelligent workpiece carrier represents an additional part for realising a cyber-physical production system (CPPS).
The supply of customer-specific products is leading to the increasing technical complexity of machines and plants in the manufacturing process. In order to ensure the availability of the machines and plants, maintenance is considered as an essential key. The application of cyber-physical systems enables the complexity to be mastered by improving the availability of information, implementing predictive maintenance strategies and the provision of all relevant information in real-time. The present research project deals with the development of a cost-effective and retrofittable smart maintenance system for the application of ultraviolet (UV) lamps. UV lamps are used in a variety of applications such as curing of materials and water disinfection, where UV lamps are still used instead of UV LED due to their higher effectiveness. The smart maintenance system enables continuous condition monitoring of the UV lamp through the integration of sensors. The data obtained are compared with data from existing lifetime models of UV lamps to provide information about the remaining useful lifetime of the UV lamp. This ensures needs-based maintenance measures and more efficient use of UV lamps. Furthermore, it is important to have accurate information on the remaining useful lifetime of a UV lamp, as the unplanned breakdown of a UV lamp can have far-reaching consequences. The key element is the functional model of the envisioned cyber-physical system, describing the dependencies between the sensors and actuator, the condition monitoring system as well as the IoT platform. Based on the requirements developed and the functional model, the necessary hardware and software are selected. Finally, the system is developed and retrofitted to a simulated curing process of a 3D printer to validate its functional capability. The developed system leads to improved information availability of the condition of UV lamps, predictive maintenance measures and context-related provision of information.
Executive education in IS is under the scrutiny of many institution for the potential to bring in financial revenues. However teaching executives can be a very challenging task because of the previous experiences, variation in their previous education, and multiplicity of motivations for pursuing a continuous education. The panel aims at sharing successful experiences and highlighting challenges of dealing with executive audiences. The panel will present the results of a large survey among executive students and identify the three most significant elements emerged from the survey: the importance of theory that is actionable, the importance of varied pedagogical tools and practices, and the importance of relevance beyond practical tools. Based on a survey that will be distributed to the audience at the beginning of the panel, the audience will be actively engaged in sharing their experiences on the three topics aiming at capitalize and sum up the collective knowledge of the room.
Development of an IoT-based inventory management solution and training module using smart bins
(2023)
Flexibility, transparency and changeability of warehouse environments are playing an increasingly important role to achieve a cost-efficient production of small batch sizes. This results in increasing requirements for warehouses in terms of flexibility, scalability, reconfigurability and transparency of material and information flows to deal with large number of different components and variable material and information flows due to small batch sizes. Therefore, an IoT-based inventory management solution and training module has been developed, implemented and validated at Werk150 – the Factory on campus of the ESB Business School. Key elements of the developed solution are smart bins using weight mats to track the bin’s content and additional sensors and buttons which are connected to an IoT – Hub to collect data of material consumption and manual handling operations. The use of weight mats for the smart bins offers the possibility to measure the container content independent of the specific component geometry and thus for a variety of components based on the specific component weights. The developed solution enables focusing on key for success elements of the system to provide synchronization of the flow of materials and information resulting an increase of flexibility and significantly higher transparency of the material flow. AIbased algorithms are applied to analyse the gathered data and to initiate process optimizations by providing the logistics decision makers a profound and transparent basis for decision making. In order to provide students and industry visitors of the learning factory with the necessary competences and to support the transfer into practice, a training module on IoT-based inventory management was developed and implemented.
Rising consumption due to a growing world population and increasing prosperity, combined with a linear economic system have led to a sharp increase in garbage collection, general pollution of the environment and the threat of resource scarcity. At the same time, the perception of environmental protection becomes more sensitive as the consequences of neglecting sustainable business and eco-efficiency become more visible. The Circular Economy (CE) could reduce waste production and is able to decouple economic growth from resource consumption, but most of the products currently in use are not designed for the reuse-forms of the CE. In addition, the decision-making process of the End of-Usage (EoU) products regarding the following steps has further weaknesses in terms of economic attractiveness for the participants, which leads to low return rates and thus the disposal is often the only alternative.
This paper proposes a model of the decision-making process, which uses machine learning. For this purpose, a Machine Learning (ML) classification is created, by applying the waterfall model. An artificial neural network (ANN) uses information about the model, use phase and the obvious symptoms of the product to predict the condition of individual components. The resulting information can be used in a downstream economic and ecological evaluation to assess the possible next steps. To test this process comprehensive training data is simulated to train the ANN. The decentralized implementation, cost savings and the possibility of an incentive system for the return of an end-of-usage product could lead to increased return rates. Since electronic devices in particular are attractive for the CE, laptops are the reference object of this work. However, the obtained findings are easily applicable to other electronic devices.
Die zunehmende Technologie- und Produktkomplexität führen dazu, dass sich immer mehr Unternehmen für ihre F&E mit externen Organisationen vernetzen. So entstehen interorganisationale F&E-Projekte, welche temporäre Organisationen darstellen. Forschungsfragen zu diesen Projekten sind u.a. hinsichtlich der Praktiken und Verhaltensregeln offen. Über ein kulturbewusstes Projektmanagement können kooperations- und innovationsförderliche Praktiken und Verhaltensregeln aufgebaut werden, die für diese F&E-Projekte essenziell sind. So ist die Forschungsfrage dieses Beitrags, wie ein projektkulturbewusstes Management interorganisationaler F&E-Projekte erfolgen kann. Dafür wird auf Basis der theoretischen Grundlagen zum F&E-Projektmanagement, zu menschlichen Handlungssystemen und Ebenen der Zusammenarbeit, zu Kultur und Verhalten ein projektkulturbewusstes Management-Modell entwickelt. Das Modell umfasst zwei Teile. Im ersten Teil wird der Bereich aufgezeigt, in welchem sich die Projektkultur entwickelt. Im zweiten Teil wird aufgezeigt, wie die Faktoren für ein wahrscheinlich kooperatives und innovatives Verhalten innerhalb dieses Bereiches gestaltet werden sollten.
Because of a high product and technology complexity, companies involve external partners in their research and development (R&D) processes. Interorganizational projects result, which represent temporary organizations. In these projects heterogenous organizations work closely together. Since project work is always teamwork, these projects face due to their characteristic’s major challenges on an organizational, relational, and content-related collaboration level. Thus, this paper raises the following research question: “How can a project team be supported on an organizational, relational, and content-related level in an interorganizational new product development setting?” To answer this research question, an explorative expert study was set up with two digital workshops using the interactive presentation tool Mentimeter. The results show that a cooperative innovation culture could support project teams on an organizational and relational level in the future in minimizing predominant problems. Moreover, it supports project teams for example in a functional communication. Furthermore, 18 values of a cooperative innovation culture result which are for example openness and transparency, risk and failure tolerance or respect. On a content-related level the results show that an adaptable tool which promotes creativity and collaboration method as well as content-related input support could be beneficial for problem-solving in an interorganizational new product development setting in the future. Because the tool can guide product developers through the process with suitable creativity and collaboration methods, can give content-related input and can enable interactive interchange on a table-top. Future research could mainly focus on the connection of the cooperative innovation culture and the tool since these potentially influence each other.
This paper takes a holistic view on an IP-traceability process in interorganizational R&D projects, as a particular Open innovation mode, aiming at showing different technologies which can be used in the front and backend of a traceability process and discussing these technologies in terms of their suitability for data from creativity processes in these projects. To achieve this goal a two-stage literature review on different technologies in the context of traceability was conducted. Then, criteria were derived from the characteristics of data from creativity processes and of interorganizational R&D projects, with which the resulting technologies were discussed. At the end, recommendations regarding suitable technologies for tracing individual creativity artifacts in interorganizational R&D projects were given.
Since project managers still face problems in managing interorganizational R&D projects, it is a promising approach to manage these projects project-culturally-aware. However, an important prerequisite for a project-culture-aware management is that the involved individual organizations pursue a collaborative strategy. Therefore, our article provides a conceptual approach including a new tool, the Collaborative Iron Triangle, which supports both project sponsors and managers in different phases of the collaboration process to pursue a collaborative strategy in interorganizational R&D projects.
The fifth mobile communications generation (5G) can lead to a substantial change in companies enabling the full capability of wireless industrial communication. 5G with its key features of providing Enhanced Mobile Broadband, Ultra-Reliable and Low-Latency Communication, and Massive Machine Type Communication will support the implementation of Industry 4.0 applications. In particular, the possibility to set-up Non-Public Networks provides the opportunity of 5G communication in factories and ensures sole access to the 5G infrastructure offering new opportunities for companies to implement innovative mobile applications. Currently there exist various concepts, ideas, and projects for 5G applications in an industrial environment. However, the global rollout of 5G systems is a continuous process based on various stages defined by the global initiative 3rd Generation Partnership Project that develops and specifies the 5G telecommunication standard. Accordingly, some services are currently still far from their final performance capability or not yet implemented. Additionally, research lacks in clarifying the general suitability of 5G regarding frequently mentioned 5G use cases. This paper aims to identify relevant 5G use cases for intralogistics and evaluates their technical requirements regarding their practical feasibility throughout the upcoming 5G specifications.
Recently, blockchain-based tokens have earned an important role in fields such as the art market or online gaming. First approaches exist, which adopt the potentials of blockchain tokens in supply chain management to increase transparency, visibility, automation, and disintermediation of supply chains. In context, the tokenization of assets in supply chains refers to the practice of creating virtual representations of physical assets on the blockchain. Solutions in supply chain management based on the tokenization of assets vary in terms of application objectives, token types, asset characteristics, as well as the complexities of supply chain events to be mapped on the blockchain. Currently, however, no review exists that summarizes the characteristics of blockchain-based tokens and their scope of applications. This paper provides a clear terminological distinction of existing blockchain token types and therefore distinguishes between fungible tokens, non-fungible tokens, smart non-fungible tokens, and dynamic smart non-fungible tokens. Subsequently, the token types are classified regarding their traceability, modifiability, and authorization to evaluate suitability for mapping assets in supply chains. Given the potential of blockchain in supply chain management, the results of the review serve as a foundation for a practical guide supporting the selection process of suitable token types for industrial applications.
The blockchain technology represents a decentralized database that stores information securely in immutable data blocks. Regarding supply chain management, these characteristics offer potentials in increasing supply chain transparency, visibility, automation, and efficiency. In this context, first token-based mapping approaches exist to transfer certain manufacturing processes to the blockchain, such as the creation or assembly of parts as well as their transfer of ownership. However, the decentralized and immutable structure of blockchain technology also creates challenges when applying these token-based approaches to dynamic manufacturing processes. As a first step, this paper investigates existing mapping approaches and exemplifies weaknesses regarding their suitability for products with changeable configurations. Secondly, a concept is proposed to overcome these weaknesses by introducing logically coupled tokens embedded into a flexible smart contract structure. Finally, a concept for a token-based architecture is introduced to map manufacturing processes of products with changeable configurations.
Classification model of supply chain events regarding their transferability to blockchain technology
(2021)
The blockchain technology represents a decentralized database that stores information securely in immutable data blocks. Regarding supply chain management, these characteristics offer potentials in increasing supply chain transparency, visibility, automation, and efficiency. In this context, first token-based mapping approaches exist to transfer certain supply chain events to the blockchain, such as the creation or assembly of parts as well as their transfer of ownership. However, the decentralized and immutable structure of blockchain technology also creates challenges. In particular, the scalability, storage capacity, and the special requirements for storage formats make it currently impossible to map all supply chain events unrestrictedly on the blockchain. As a first step, this paper identifies important supply chain events for different use cases combining blockchain technology and supply chain management. Secondly, the supply chain events are classified in terms of their expected technical properties and their relevance for the respective use case. Finally, the identified supply chain events are evaluated regarding their transferability to blockchain technology and a classification model is introduced.
Blockchain is a technology for the secure processing and verification of data transactions based on a distributed peer-to-peer network that uses cryptographic processes, consensus algorithms, and backward-linked blocks to make transactions virtually immutable. Within supply chain management, blockchain technology offer potentials in increasing supply chain transparency, visibility, automation, and efficiency. However, its complexity requires future employees to have comprehensive knowledge regarding the functionality of blockchain-based applications in order to be able to apply their benefits to scenarios in supply chain and production. Learning factories represent a suitable environment allowing learners to experience new technologies and to apply them to virtual and physical processes throughout value chains. This paper presents a concept to practically transfer knowledge about the technical functionality of blockchain technology to future engineers and software developers working within supply chains and production operations to sensitize them regarding the advantages of decentralized applications. First, the concept proposes methods to playfully convey immutable backward-linked blocks and the embedment of blockchain smart contracts. Subsequently, the students use this knowledge to develop blockchain-based application scenarios by means of an exemplary product in a learning factory environment. Finally, the developed solutions are implemented with the help of a prototypical decentralized application, which enables a holistic mapping of supply chain events.
Distributed ledger technologies such as the blockchain technology offer an innovative solution to increase visibility and security to reduce supply chain risks. This paper proposes a solution to increase the transparency and auditability of manufactured products in collaborative networks by adopting smart contract-based virtual identities. Compared with existing approaches, this extended smart contract-based solution offers manufacturing networks the possibility of involving privacy, content updating, and portability approaches to smart contracts. As a result, the solution is suitable for the dynamic administration of complex supply chains.
Companies are becoming aware of the potential risks arising from sustainability aspects in supply chains. These risks can affect ecological, economic or social aspects. One important element in managing those risks is improved transparency in supply chains by means of digital transformation. Innovative technologies like blockchain technology can be used to enforce transparency. In this paper, we present a smart contract-based Supply Chain Control Solution to reduce risks. Technological capabilities of the solution will be compared to a similar technology approach and evaluated regarding their benefits and challenges within the framework of supply chain models. As a result, the proposed solution is suitable for the dynamic administration of complex supply chains.
Public transport causes in rural areas high costs per passenger and kilometer as the frequency of scheduled busses is low and therefore, many people avoid using public transport. With the trend of moving from urban regions to countryside individual traffic will further increase. To tackle issues of emissions, mobility for young and elderly people and provide economically meaningful public transport a new concept was elaborated in Germany. This consists of (partly) autonomous shuttle busses which are remote controlled. For implementation rural districts of Germany have worked together and set up a three-phase plan consisting of a project with public funding, a highly frequent used pilot region and industrial partners with the commitment and possibilities for necessary investments. The concept promises economical value with respect to installation, service and maintaining costs, it leads to lower barriers for public transport of young and elderly people and ultimately reduces emissions and congestions.
Process risks are omnipresent in the corporate world and repeatedly present organizations with the challenge of how to deal with these risks. Efforts in trying to analyze and prevent these risks are costly and require many resources, which do not always bring the desired added value. The goal of this work is to determine how a benefit-oriented resource allocation can be made for risk-oriented process management. For this purpose, the following research question is posed: "How can systematic prioritization decisions regarding risk-oriented process management be made?” To answer it, an evaluation procedure is developed which assesses processes based on their characteristics regarding potential risk disposition as well as entrepreneurial relevance. For this purpose, requirements for such a procedure are first collected and used to define selection criteria for it. After the detailed analysis of known selection and evaluation procedures, one of them is selected and used for further development. Next steps include the definition of relevant criteria for the evaluation of the processes by examining process characteristics regarding their suitability for process evaluation. The focus here lies on characteristics that provide indications of the risk disposition and business relevance of processes. The result of this approach is a scoring model with a criteria catalog consisting of 15 criteria according to which a process is evaluated. The evaluation result is presented both numerically and in a matrix. This enables the comparison of several processes and a derived prioritization of those for a more in-depth risk analysis. The application of this approach will ensure a benefit-oriented allocation of resources in the management of process risks and increased process reliability.
The production environment experiences copious challenges, but likewise discovers many new potential opportunities. To meet the new requirements, caused by the developments towards mass-customization, human-robot-cooperation (HRC) was identified as a key piece of technology and is becoming more and more important. HRC combines the strengths of robots, such as reliability, endurance and repeatability, with the strengths of humans, for instance flexibility and decision-making skills. Notwithstanding the high potential of HRC applications, the technology has not achieved a breakthrough in production so far. Studies have shown that one of the biggest obstacles for implementing HRC is the allocation of tasks. Another key technology that offers various opportunities to improve the production environment is Artificial Intelligence (AI). Therefore, this paper describes an AI supported method to improve the work organization in HRC in regards to the task-allocation. The aim of this method is to build a dynamic, semi-autonomous group work environment which keeps not just employee motivation at a high level, but also the product quality due to a decreased failure rate. The AI helps to detect the perfect condition in which the employee delivers the best performance and also supports at identifying the time when the worker leaves this optimal state. As soon as the employee reaches this trigger event, the allocation of the tasks adapts based on the identified stress. This adaptation aims to return the employee to the state of the optimal performance. In order to realize such a dynamic allocation, this method describes the creation of a pool with various interaction scenarios, as well as the AI supported recognition of the defined trigger event.
Die Digitalisierung und Nachhaltigkeit werden die Erwartungen und Anforderungen an die Controller dauerhaft und umfassend verändern. Die Lehre hat für den Rollenwandel eine hohe Relevanz. Eine auf die veränderten Anforderungen abgestimmte Ausbildung bietet den Unternehmen die Möglichkeit, Controller mit diesen veränderte Rollenprofilen für ihre Organisation zu gewinnen. Für die Absolventen mit dem Berufswunsch Controlling sichert das veränderte Rollenprofil ihre langfristige Arbeitsmarktfähigkeit. Für den Rollenwandel selbst kann diese als Treiber verstanden werden.
Trotz der Bedeutung der Lehre für den Rollenwandel gibt es dazu bislang wenige Forschungsergebnisse zur konkreten Abbildung der Rollen in der Lehre. Es stellt sich daher die Frage, wie Hochschulen in ihren Studiengängen die Rollen grundsätzlich abbilden und mit welcher Intensität sowie Kombinationen die Rollen gelehrt werden. Diese Forschungsfrage wird anhand einer Analyse von controllingspezifischen Masterstudiengängen und deren Modulhandbücher evaluiert und diskutiert.
Im Ergebnis stellt sich der Rollenwandel in der Controllinglehre sehr heterogen dar. Es dominiert die Vermittlung der klassische Controllerrolle gefolgt von der Business Partner Rolle. Lehrinhalte bezogen auf die Rollen des digitalen Controllers oder Risikocontrollers sind schwach ausgeprägt. Für die Übernahme einer Controllerrolle im Nachhaltigkeitsmanagement existiert kaum ein Lehrangebot. Diese Ergebnisse sollen zum Diskurs über den Rollenwandel und die Gestaltung der Lehre im Controlling beitragen.
This paper describes the design and outcomes of an experimental study that addresses stock-and-flow-failure from a cognitive perspective. It is based on the assumption that holistic (global) and analytic (local) processing are important cognitive mechanisms underlying the ability to infer the behavior of dynamic systems. In a stock-and-flow task that is structurally equivalent to the department store task, we varied the format in which participants are primed to think about an environmental system, in particular whether they are primed to concentrate on lower-level (local) or higher-level (global) system elements. 148 psychology, geography and business students participated in our study. Students’ answers support our hypothesis that global processing increases participants’ ability to infer the overall system behavior. The beneficial influence of global presentation is even stronger when data are presented numerically rather than in the form of a graph. Our results suggest presenting complex dynamic systems in a way that facilitates global processing. This is particularly important as policy-designers and decision makers deal with complex issues in their everyday and professional life.
A shift in attitudes to purchasing departments can be perceived. No longer is the chief goal solely to reduce costs; the procurement function is assuming strategic relevance in the business model, leveraging the supplier as a foundation for innovation. The knowledge accumulated by suppliers is accessed over the journey of long-term partnerships to streamline business practice. Businesses are finding themselves in increasingly competitive environments, and thus need to address inefficiencies in supplier management. “Procurement 4.0” is a concept used in discussing digitalisation in business processes, referring to the process of supplier relationship management and optimisation. This model and its application to supplier relationship management will be the focus of this article. Realising the efficiencies to be obtained in buyer-supplier relationships through “Procurement 4.0” will be explored, primarily through an emphasis on digitalisation of the relationship between the procurement department and supplier.
At Reutlingen University in Germany students from different countries and disciplines can learn business English within the framework of a theatre production. In the "Business English Theatre" they work in an international project team staging a play with a business focus and thus improve both their language, social and professional skills.
Indoor localization systems are becoming more and more important with the digitalization of the industrial sector. Sensor data such as the current position of machines, transport vehicles, goods or tools represent an essential component of cyber physical production systems (CCPS). However, due to the high costs of these sensors, they are not widespread and are used mainly in special scenarios. However, especially optical indoor positioning systems (OIPS) based on cameras have certain advantages due to their technological specifications. In this paper, the application scenarios and requirements as well as their characteristics are presented and a classification approach of OIPS is introduced.
Intermittent time series forecasting is a challenging task which still needs particular attention of researchers. The more unregularly events occur, the more difficult is it to predict them. With Croston’s approach in 1972 (1.Nr. 3:289–303), intermittence and demand of a time series were investigated the first time separately. He proposes an exponential smoothing in his attempt to generate a forecast which corresponds to the demand per period in average. Although this algorithm produces good results in the field of stock control, it does not capture the typical characteristics of intermittent time series within the final prediction. In this paper, we investigate a time series’ intermittence and demand individually, forecast the upcoming demand value and inter-demand interval length using recent machine learning algorithms, such as long-short-term-memories and light-gradient-boosting machines, and reassemble both information to generate a prediction which preserves the characteristics of an intermittent time series. We compare the results against Croston’s approach, as well as recent forecast procedures where no split is performed.