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Industrial Service Providers (ISP) are exposed to constantly raising competitive pressures regarding both cost and performance aspects. The massive challenges caused by the current worldwide financial and economic crisis even intensified the need for process optimizations aimed at increasing the productivity of service production. To reach this goal the evaluation and elimination of waste in their production processes becomes a crucial ability for ISPs. This paper proposes a new approach for increasing productivity in service production processes using a generic measurement model for the detection and evaluation of waste. The model is based on established lean management principles, but tailored to the specifics of ISPs by adopting a customers’ perspective to track down and eliminate waste. The evaluation builds on an in-depth-analysis of particular types of waste in the industrial service production processes. Viewed from the customers’ perspective and taking into account the specific characteristics of services (e.g. intangibility, heterogeneity, inseparability, and perishability) and service production (e.g. volatile demand, a tendency to over-capacity, and limits to planning) the approach employs a service blueprint reference model to then determine the different types of waste in the various parts of the service production process.
As industrial service portfolios grow, many companies overlook the implications of their business operations: rising complexity and resulting complexity costs. One reason are nonexistent tools that help service managers to decide in planning phases with an adequate effort about the implications that variety and complexity decisions have on the complexity costs of their portfolio. This paper depicts the challenges service companies have to face in this context and presents a concept of a heuristic approach to evaluate the complexity costs for industrial services. The concept is being developed in strong cooperation with industrial partners.
Volatile electricity prices caused by an increase of renewable energy sources push producing companies towards taking in an active role in balancing the electricity grid. Possible actions at the customer side to actively adapt to volatile energy prices are called demand response actions. In production logistics such actions can be the modification of production schedules motivated by possible economic benefits. So far, the focus in scheduling problems has been the optimization in the dimensions of quality, time and costs. This paper presents the results of a simulation study on the economic benefits of demand response actions for a generic production system.
This paper presents a simulation approach for service production processes on the basis of which an optimal operating point for service systems can be identified. The approach specifically takes into account the characteristics of human behavior. The simulation is based on a system theory approach to the service delivery process. A specific use case of the simulation approach is presented in detail to illustrate how characteristic curves are deduced and an optimal operating point is obtained.
Numerous traditional, agile and hybrid development approaches have been proposed for the development of CPS. As the choice of development process is crucial to the success of development projects, it has become a major challenge to identify the best-suited process. This paper introduces a methodology for identifying the best-suited CPS development process, based on the individual boundary conditions for a certain development project within a company. The authors used a set of eight indicators to assess a CPS-development project. The results of the assessment were matched with CPS-development approaches. Based on the matching results a best-suited development process was selected. The application is shown for a use case in the German manufacturing industry. The developed method aims to reduce the risk of project failure due to the wrong choice of development process.
Digital technologies such as 5G, augmented reality, and artificial intelligence (AI) are currently being used in various ways by manufacturing companies. As the fourth industrial revolution progresses, it has become apparent that reckless use and inadequate regulation of these technologies have a detrimental effect on the environment in which they are utilized. Therefore, regulation of digital technologies is imperative today to ensure more responsible and sustainable use. While governments usually establish regulations, progress is not keeping pace with the demands and hazards of employing digital technologies. The European AI law serves as an example of the considerable distance yet to be covered before binding guidelines are established. Consequently, companies must take proactive measures today to ensure that they use digital technologies responsibly in their environments. In this context, identifying which digital technologies are pertinent to manufacturing companies in terms of regulation is crucial. Furthermore, a comprehensive approach is required to design compliance holistically for digital technologies and to systematically derive the corresponding guidelines. This paper introduces a set of models that not only determine the importance of
compliance in the application of different technologies but also present a framework for methodically designing compliance. Furthermore, the paper contributes to the development of an AI platform in the German research project PAIRS by investigating the compliance relevance of applications such as artificial intelligence.
Methods of machine learning (ML) are notoriously difficult for enterprises to employ productively. Data science is not a core skill of most companies, and acquiring external talent is expensive. Automated machine learning (Auto-ML) aims to alleviate this, democratising machine learning by introducing elements such as low-code / no-code functionalities into its model creation process. Multiple applications are possible for Auto-ML, such as Natural Language Processing (NLP), predictive modelling and optimization. However, employing Auto-ML still proves difficult for companies due to the dynamic vendor market: The solutions vary in scope and functionality while providers do little to delineate their offerings from related solutions like industrial IoT-Platforms. Additionally, the current research on Auto-ML focuses on mathematical optimization of the underlying algorithms, with diminishing returns for end users. The aim of this paper is to provide an overview over available, user-friendly ML technology through a descriptive model of the functions of current Auto-ML solutions. The model was created based on case studies of available solutions and an analysis of relevant literature. This method yielded a comprehensive function tree for Auto-ML solutions along with a methodology to update the descriptive model in case the dynamic provider market changes. Thus, the paper catalyses the use of ML in companies by providing companies and stakeholders with a framework to assess the functional scope of Auto-ML solutions.
Gap Analysis for CO2 Accounting Tool by Integrating Enterprise Resource Planning System Information
(2023)
Detailed carbon accounting is the foundation for reducing CO2 emissions in manufacturing companies. However, existing accounting approaches are primarily based on manual data preparation, although manufacturing companies already have a variety of IT systems and resulting data available. The gap analysis carried out based on the GHG Protocol and an reference ERP system shows how much of the required information for CO2 accounting can be integrated from an ERP system. The ERP system can cover 20 % of the required information. The information availability can be increased to 49 % through additionally identified modifications of the ERP system. Integrating the CO2 accounting tool with other systems of the IT landscape, e. g. Energy Information System, enables an additional increase.
Generation of a Data Model For Quotation Costing Of Make To Order Manufacturers From Case Studies
(2022)
For contract or make to order manufacturers, quotation costing is a complex process that is mainly performed based on experience. Due to the high diversity of the product range of these mostly small or medium-sized companies (SMEs) and the poor data situation at the time of quotation preparation, the quality of the calculation is subject to strong variations and uncertainties. The gap between the initial quotation costing and the actual costs to be spent (pre- and post-calculation) is crucial to the existence of SMEs. Digitalization in general can help companies to get a better understanding of processes and to generate data. For improving these processes, an understanding of the important data for that specific process is crucial. Accurate quotation costing for customized products is time-consuming and resource-intensive, as there is a lack of an overview of data to be used within the process. This paper therefore derives a data model for supporting quotation costing in the company, based on literature-based costing procedures and recorded case studies for quotation and calculation. Based on the results, SMEs will have a first overview of the needed data for quotation costing to optimize their calculation process.
Die Herausforderungen der Zukunft werden geprägt durch digital veredelte Produkte von höchster Qualität und hoher Variantenvielfalt bei gleichzeitig kleiner werdenden Losgrößen. Konventionelle Entwicklungsmethoden stoßen aufgrund zunehmender Komplexität und kürzer werdender Lebenszyklen auf Produktebene an ihre Grenzen. Dadurch werden bei kundenindividueller Produktion die Aufwände in der Arbeitsplanung und -vorbereitung überproportional größer. Eine mögliche Lösung stellt die generative Erstellung der Produktionsstückliste während der Montage dar. Durch das eventbasierte „Mitschreiben der Montage“ werden administrative und planungsintensive Prozesse in der Arbeitsvorbereitung überproportional reduziert und die Erstellung der Stückliste in die manuelle Montage transferiert.
The manufacturing industry has to exploit trends like “Industrie 4.0” and digitization not only to design production more efficiently, but also to create and develop new and innovative business models. New business models ensure that even SMEs are able to open up new markets and canvass new customers. This means that in order to stay competitive, SMEs must transform their existing business models.
The creation of new business models require smart products. The required data base for new business models cannot be provided by SMEs alone, whereas smart products are able to provide a foundation, given the creation of smart data and smart services they enable. These services then expand functions and functionality of smart products and define new business models.
However, the development of smart products by small and medium-sized enterprises is still lined with obstacles. Regarding the product development process the inclusion of smart products means that new and SME-unknown domains diffuse during the process. Although there are many models regarding this process there appears to be a substantial lack of taking into account the competencies enabled by the implementation of digital technologies. Hence, several SME-supporting approaches fail to address the two major challenges these enterprises are faced with. This paper generally describes valid objectives containing relevant stakeholders and their allocation to the phases of the product life cycle.
Within each objective the potential benefit for customers and producers is analyzed. The model given in this paper helps SMEs in defining the initiation of a product development project more precisely and hence also eases project scoping and targeting for the smartification of an already existing product.
The efficient dealing with the dynamic environment of production industries is one of the most challenging tasks of Supply Chain Management in high-wage countries. Relevant and current information are still not used sufficiently, to handle the influence of the dynamic environment on intra- and inter-company order processing adequately. Among other things, the problem is caused by missing or delayed feedback of relevant data. As a consequence of that, planning results differ from the actual situation of production. High Resolution Supply Chain Management describes an approach aiming on high information transparency in supply chains in combination with decentralized, self-optimizing control loops for Production Planning and Control. The final objective is to enable manufacturing companies to produce efficiently and to be able to react to order-variations at any time, requiring process structures to be most flexible.
Holistic PLM- Model
(2010)
Product Lifecycle Management (PLM) is a widely discussed topic concerning the increase of efficiency of product development in terms of time to market as well as customizing products to the different needs of customers worldwide adequately. Historically PLM focuses the early phases of the product’s lifecycle, namely the product development phase. Therein the roots of PLM are based in supporting the information logistics of product data: Consistent data sets should be available to all stakeholders in the different departments at all times. Due to the increasing product complexity PLM has to be extended in terms of the temporal dimension (not limited to product development phase) and systemic dimension (not limited to the information logistic aspect). In this paper the authors derive a holistic framework for Product Lifecycle Management by analysing existing integrated management approaches. The framework consists of four dimensions: PLM strategy, PLM process, Product structure and PLM IT-Architecture. The sustainability and benefits of the framework is demonstrated by applying the framework to the communication service provider industry (CSP).
Reinforced through the pandemic and shaped by digitalization, today's professional working environment is in a state of transformation. Working remotely has become a vital component of many professions' regular routines. The design of remote work environments presents challenges to organizations of all sizes. By providing a classification, this paper reveals a comprehensive understanding of the fields of design to be considered to establish lasting remote work concepts in organizations. A hierarchical classification with four dimensions consisting of human, technology, organization, and culture, seven design elements and, twenty design parameters indicates to organizations the fields of design that need to be examined. To satisfy both the theoretical foundation and the practical application, design elements are derived by implementing a systematic review of the literature that represents key areas of interest for remote work. Additionally, these are verified and complemented by a dedicated case study research to incorporate practice-oriented design parameters.
Human behavior in supply chains is insufficiently explored. Wrong decisions by decision makers leads to insufficient behavior and lower performance not only for the decision maker, but also for other stakeholders along the supply chain. In order to study the complex decision situation, we developed a supply chain game in which we studied experimentally the decisions of different stakeholder within the chain. 121 participants took part in a web-based supply chain game. We investigated the effects of gender, personality and technical competency on the performance within the supply chain. Also, learnability and the effect of presence of point-of-sale data are investigated. Performance depended on the position within the chain and fluctuating stock levels were observed in form of the bullwhip effect. Furthermore, we found that risk taking had an impact on the performance and that the performance improved after the first round of the game. [https://link.springer.com/chapter/10.1007/978-3-642-39226-9_46]
In the age of digitalization, manufacturing companies are under increased pressure to change due to product complexity, growing customer requirements and digital business models. The increasing digitization of processes and products is opening up numerous opportunities for mechanical engineering companies to exploit the resulting potential for value creation. Subscription business is a new form of business model in the mechanical engineering industry, which aims to continuously increase customer benefit to align the interests of both companies and customers. Characterized by a permanent data exchange, databased learning about customer behavior, and the transfer into continuous innovations to increase customer value, subscription business helps to make Industry 4.0 profitable. The fact that machines and plants are connected to the internet and exchange large amounts of data results in critical information security risks. In addition, the loss of knowledge and control, data misuse and espionage, as well as the manipulation of transaction or production data in the context of subscription transactions are particularly high risks. Complementary to direct and obvious consequences such as loss of production, the attacks are increasingly shifting to non-transparent and creeping impairments of production or product quality, which are only apparent at a late stage, or the influencing of payment flows. A transparent presentation of possible risks and their scope, as well as their interrelationships, does not exist. This paper shows a research approach in which the structure of subscription models and their different manifestations based on their risks and vulnerabilities are characterized. This allows suitable cyber security measures to be taken at an early stage. From this basis, companies can secure existing or planned subscription business models and thus strengthen the trust of business partners and customers.
Auf Basis einer systematischen Literaturanalyse wurden insgesamt 11 Kennzahlen identifiziert, welche die Grundlage zur Beschreibung der operativen Leistungsfähigkeit von Unternehmen bilden. Die Kennzahlen wurden in die vier Leistungsdimensionen Effizienz, Qualität, Zeit und Flexibilität eingeteilt.
Manufacturing companies face the challenge of managing vast amounts of unstructured data generated by various sources such as social media, customer feedback, product reviews, and supplier data. Text-mining technology, a branch of data mining and natural language processing, provides a solution to extract valuable insights from unstructured data, enabling manufacturing companies to make informed decisions and improve their processes. Despite the potential benefits of text mining technology, many manufacturing companies struggle to implement use cases due to various reasons. Therefore, the project VoBAKI (IGF-Project No.: 22009 N) aims to enable manufacturing companies to identify and implement text mining use cases in their processes and decision-making processes. The paper presents an analysis of text mining use cases in manufacturing companies using Mayring's content analysis and case study research. The study aims to explore how text mining technology can be effectively used in improving production processes and decision-making in manufacturing companies.
Nowadays, providing purchasable goods is not enough for a company to survive on the global market. Because of competitive prices and a large range of products available, companies need to offer additional benefits to their customers in order to create a unique selling point. They add services to their product portfolio and offer clients the opportunity to acquire an additional service solution to go with it. The offered services need to fit to the customer's needs, resulting in a variety of available services, great complexity of the service range and decreasing transparency of the resource utilization. This paper addresses the problem by identifying variant-creating factors in product service systems, transferring them into an organizational framework and verifying their significance.
In order to introduce load management in the manufacturing industry, some obstacles need to be pointed out. This paper presents a feasible approach on how to implement load management measures in companies. To do so, load management and energy management are explained and distinguished in a first step. Subsequently, the implementation method is introduced. Therefore, by using this paper, companies will be enabled to use load management measure and reduce their energy costs significantly.