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Pricing for Smart-Product-Service-Systems in Subscription Business Models for Production Industries
(2021)
In the production industry, subscription business models have the potential to create long-term relationships where a supplier provides a continuous value-oriented service to a customer based on digitalisation. Monetising this increase in value through pricing represents a central challenge for suppliers in subscription business. Unlike the current dominant transactional business, the focus of pricing is on the value-in-use of the customer (e.g. on the increase in output for the customer). In this regard, there is so far no pricing approach for practice that allows the linking of the performance data of the customer with the periodically charged price. However, in subscription businesses, such an approach is required to create win-win situations for the customer and supplier through continuous performance improvement. Therefore, this paper develops a novel process model for pricing of smart-product-service-systems in subscription business for production industries. This process can serve as basis for suppliers of subscriptions in the production industry to align pricing with the created value-in-use. In the long term, this allows companies to systematically develop their pricing to monetise the potential of digitalisation.
The Impact Of Manufacturing Execution Systems On The Digital Transformation Of Production Systems
(2021)
With the focus of manufacturing companies on the digital transformation, Manufacturing Execution Systems are market-ready, modular software solutions for manufacturing companies to integrate the value-adding and supporting processes horizontal and vertical in the company. Companies, especially small and mediumsized companies, face high internal and external costs for the implementation of the MES modules. An advantage of MES is the possibility to implement the systems in a continually, module-by-module approach, with the benefit of timely distributed investments. By realizing fast improvements, companies can use the benefits for further module implementations. This paper proposes a maturity model to measure the impact of an MES on the digital transformation of the company’s production systems. The model fulfils two purposes. The first, companies can measure the impact based on the difference between its current maturity index and the potential index of an implemented MES. The second is, the user can identify what impact an MES has in general on the digital transformation since the developed maturity model is derived from an established industry 4.0 maturity model. The development of the maturity model is based on the methodologies of AKKASOGLU and focuses on the further development of an established model. As an outlook, the application of the model will be described briefly. The proposed maturity model can directly be used by practitioners and offers implications for further development of MES functionalities.
Industrial practice shows a strong trend towards digitalization. It is not only economic crises, such as those triggered by Covid-19, that are reinforcing this trend. It is also the entrepreneurial urge to fulfill customer wishes in the best possible way and to adapt to new requirements as quickly as possible. Due to the advancing digitalization, the role of business application systems in manufacturing companies is therefore becoming increasingly important. The data processed in IT-Systems represent a great potential, especially for the evaluation of change requests in production. Through efficient change management, companies can record and process changes quickly. However, the necessary data basis to decide on existing change requests is still hardly used. Existing IT-Systems for change management coordinate the processing of change requests, but do not relate to data of operational application systems such as Enterprise-Resource-Planning. Therefore, a conceptual approach is required for the evaluation of change requests. This approach is based on an objective recording system that enables the transformation from the change description to an evaluation space. The paper presents an approach for the systematic transfer of requirement characteristics into the world of operational IT-Systems.
Manufacturing companies (MFRs) are increasingly extending their
portfolios with services and data-driven services (DDS) to differentiate themselves from competitors, tap new revenue potential, and gain competitive advantages through digitization and the subsequently generated data. Nonetheless, DDS fail more often than traditional industrial services and products within the first year on the market. Particularly, companies are failing to sell DDS successfully and efficiently with their existing (multi-level) distribution structures. Surprisingly, there is a lack of scientific research addressing this issue. Since there are currently no holistic models for an end-to-end description of distribution-tasks for DDS in the manufacturing industry, this paper contributes to a task-oriented reference model for mapping interactions in the multi-level distribution management. Therefore, a case study research approach is used, to identify and describe the interactions in the multi-level distribution management of DDS, as well as to develop a regulatory framework for MFRs and their multi-level distribution management. This research uses the established theoretical framework of Service-Dominant-Logic to address the co-creation in multi-level distribution management of DDS. As a result, this paper identifies different interaction variants as well as the need for a new management function with 4 main and 14 basic tasks.
In order to achieve a holistic cost management approach, the maintenance and service costs should already be assessed during the development of machines and equipment. The required information in the company, like PLM, process and test data, are commonly not available or vague, especially in early development phases. This paper introduces a feasible method for an early assessment of maintenance and service costs during product development. In doing so, appropriate cost assessment methods are selected, based on the availability and quality of the existing information in the individual development phases. The evaluations of these methods are aggregated in a software tool, so that the respective cost information is displayed with a maximum, minimum and most probable value. The developed software tool was validated in cooperation with a new electric vehicle manufacturer.
Management of information and the IT systems it is stored in becomes a crucial capability for the industry. However, companies are struggling with the management of the various requirements and frequent changes of technology. Thus, IT complexity has become a major challenge for companies. At the same time, especially manufacturing companies are striving to implement Industrie 4.0 concepts. Many of these even have developed an Industrie 4.0 roadmap including various projects to change the company. Companies can develop such roadmaps by applying the Industrie 4.0 Maturity Index that gives a broad view on necessary capabilities for Industrie 4.0.
In our research, we analyzed data sets from over 10 manufacturing companies that have performed an Industrie 4.0 maturity assessment. Our hypothesis was that IT complexity challenges are hindering the implementation of Industrie 4.0 roadmaps significantly. We could prove this hypothesis at least for the companies analyzed and give insights on the specific challenges. Based on our analysis, we conclude our article by giving concrete recommendations on how to tackle IT complexity.
Working capital management is one of the key disciplines that must be prudently monitored for a firm in pursuit of profits, liquidity and growth. The focus of this paper is on the engineer-to-order manufacturers, and the objective is to analyze the correlations between the reference processes of the engineer-to-order production approach with the key postulates of working-capital management and deliver a mathematical operating curves model, whose purpose and goal is basing on the rationale, that is underlying in the parent logistic operating curves theory. [https://link.springer.com/chapter/10.1007/978-3-319-66926-7_30]
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]
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.
The main challenge in all application areas of EV usage still is the energy storage within, as well as the energy transmission into an EV. However, this storage and transmission of energy also allows for synergies with a smart grid, if the information is adequately exchanged between roles in the energy and mobility sector. Since the energy transmission is a so called “fixed and intersection point” of E-Mobility, interoperability is required not only on an electrical (e.g. plugs), but also on an informational level. Standardization efforts are currently underway (e.g. IEC 15118), yet a comprehensive, consolidating view on the information system around energy transmission is missing. Therefore, this paper suggests a generic information system architecture for e-mobility (EM-ISA) derived from the Smart Grid Architecture Model (SGAM). EM-ISA shall be a base for companies to develop innovative services for their particular, ICT-enabled E-Mobility application area while at the same time stay at important points informational interoperable at the fixed and intersection point of energy transmission.
In recent years supply chain participants are increasingly suffering the effects of disturbances in transportation supply chains. Both, dynamics in consumer demands and global supply chains lead to a growth in unplanned supply chain events. These can cause from rather manageable disturbances through to complete break-downs of transportation chains, resulting in high follow-up and penalty costs.
Consequently, concepts for an efficient supply chain disturbance management are needed, preferably with a real-time identification and reaction to disturbance events. Therefore in the following paper the research results of the German research project Smart Logistic Grids with the focus on designing an integrated model for the real-time disturbance management in transportation supply networks are presented. This includes the introduction of elaborated classification models for disturbances and action patterns as well as an associated costs and performance measurement system. Finally, a procedure model for the disturbance management is presented.
The steady increasing of supply chain complexity due to a rising global cross-linking of production and sales regions leads to an increasing sensitivity to disturbances while in the meantime the requirements of the availability, the time of delivery and the security of supplies within the supply chain increases. To meet this challenges the security of the supply chain infrastructure and the feasibility of supply chain processes need to be ensured, despite of the high specialization within the supply chain partners, the low stock and time buffers, and the information shortcoming between supply chain partners.
In this research, a System Dynamics simulation model, based on the manufacturing supply chain model of Sterman, has been developed for representing the actual complexity and dynamic in manufacturing supply chains. Therefore, the modeled manufacturing supply chain shows the processes of a four level supply chain focusing the processes and interactions of the mid-positioned two supply chain participants. The main contribution of the work described in this paper, is the description and implementation of necessary additional modules and parameters to Sterman’s basic model for the diagnosis of disturbance impacts as well as for the realization of supply chain adjustments. Finally, the model has been simulated and examined for realistic values.
Systematization models for taylor-made sensor system applications and sensor data fit in production
(2015)
Industrial digitalization to realize smart factories is driven by an informatory base of high-resolution data provided by sensor systems on the shop-floor level. The challenge of technical availability of fitting measurement solutions nowadays turns in a struggle of finding the optimal solution for a specific task in an ever-growing sensor market. This paper analyzes and specifies necessary models to systematically derive and describe organizational, technical and informatory requirements for sensor system applications increasing the technological fit for faster integration and lower misinvestment rates.
Systematization models for taylor-made sensor system applications and sensor data fit in production
(2015)
Industrial digitalization to realize smart factories is driven by an informatory base of high-resolution data provided by sensor systems on the shop-floor level. The challenge of technical availability of fitting measurement solutions nowadays turns in a struggle of finding the optimal solution for a specific task in an ever-growing sensor market. This paper analyzes and specifies necessary models to systematically derive and describe organizational, technical and informatory requirements for sensor system applications increasing the technological fit for faster integration and lower misinvestment rates.
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.
Companies in the manufacturing industry are shifting towards a more service-oriented business model. One major challenge of this transformation is the information exchange between the different stages of the product-service-lifecycle.
We extend the existing body of knowledge by conducting an empirical study in the German manufacturing industry, addressing the cause-effect relationship between 1) information gathering over the product-service-lifecycle, 2) data analytics 3) interpretation and use of new information and 4) distribution of new product related information and the impact of these four aspects on performance.
The analysis reveals five different success factors with a significant impact on innovation and operation excellence. The implications from our research can help to develop new and more practical oriented Lifecycle-Product-Service-System approaches on the one hand. On the other hand it enables companies to focus on activities leading to higher service efficiency. Creating new stimuli will transform their existing business model to a more service-oriented one.
Companies are transforming from transactional sales to providing solutions for their customers. Mostly, smart products, enabling companies to enhance their products by providing smart services to their customers, are a key building block in this transformation. However, the development of a smart product requires many digital skills and knowledge, which regular companies do not have. To facilitate the design and conceptualization of smart products, this paper presents a use-case-based information systems architecture prototype for smart products. Furthermore, the paper features the application and evaluation of the architecture on two different smart product projects. The use of such an architecture as a reference in smart product development serves as a huge advantage and accelerator for inexperienced companies, allowing faster entry into this new field of business. [https://link.springer.com/chapter/10.1007/978-3-031-14844-6_16]
Ongoing digitalization and Industry 4.0 enable the development of new business models due to the increase in available data and digital connected products. A promising business model type for the machinery and plant engineering industry are subscription models, consisting of products and services offered in return for continuous payments. However, subscription-based business models are associated with extensive changes in the traditional machinery and plant engineering industry, in particular, for small and medium-sized companies (SMEs). Established concepts for the development of value propositions and business models neglect important aspects, such as the integrated development and optimization of products and services across the entire life cycle or the data infrastructure. This paper presents a concept for a methodology to support SMEs developing value propositions within subscription models. Therefore, the systematic identification of customer benefits, the determination and prioritization of subscription relevant functionalities as well as the design of product and service elements addressing those functionalities are the main aspects on which the focus is placed on. The result is a subscription value proposition canvas for SMEs to address the impact of subscription models on products and services.