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In recent years, the complexity of the management of supply chains has increased significantly due to the growing individualization of products and dynamics of the market environment. To remain competitive, ensuring efficient and flexible processes and procedures along the entire supply chain are of particular importance for companies. Especially in the inter-company context, decisions must be made as quickly and correctly as possible. To enable good decision-making processes data must be processed and provided in a targeted manner. Currently, however, the necessary transparency is often lacking within the supply chains. In this article, a software-based assistance system for decision support on supply chain level is presented that aims to increase the transparency and efficiency of the decision-making process. A concept for decision support on supply chain level is presented. This paper focuses on the conceptual linkage of relevant decisions and data. Therefore, indicators are identified and linked with the relevant decisions. Moreover, a suitable way of visualizing the identified indicators for each decision in a user-friendly manner is defined. These results are then used to implement the software tool.
The industrial food production is currently caught between the increas-ing demands of numerous stakeholders, economic profitability and the challenges of digitization. A solution to face these various challenges can be seen in the aggregation of data into higher-value, independent data products that can be of-fered and sold on a buyer's market. Large amounts of heterogeneous data are already available in the value chain of the industrial food production, e.g. throughout the data-driven harvesting of primary products, further processing by interconnected production facilities and the information-intensive product distri-bution to end consumers. However, the data is usually only evaluated and used locally for the optimization of internal processes or, at the most, within compre-hensive partnerships. The purpose of this paper is to identify new revenue oppor-tunities for current and future players in the industrial food production by using data as an independent economic good (data products). For this purpose, scenar-ios for the development and use of data products via Industrial Internet of Things platforms are developed for a food technical reference process, the industrial chocolate production and its value chain. On this basis, examples for different types of data products and their value propositions are derived. The results can not only serve food producers and relevant stakeholders but all industrial produc-ers as an input for the future, yield-increasing orientation of their business models.
Many ERP systems support configurable materials. Due to an ever increasing number of product variants the benefits of this approach are well understood. However, these implementations are not standardized. In this article we propose a new standard interface for the exchange of configuration data. This would lead to further benefits as systems as Advanced Planning systems could better use manufacturing flexibility while web shops as Amazon could easily integrate manufacturers of complex products with much reduced implementation effort.
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.
One major problem of today’s producing companies is to reach a high adherence to delivery dates while considering the volatile market situation as well as economic aspects. This problem can only be solved by using a production control that is optimally adapted to the processes. A good working, process-oriented production control is essential for being able to control the production situation and to ensure a high adherence to delivery dates. Data generation and processing determine the success of production control. Current processes and IT systems have several shortcomings in meeting these challenges.
The solution for this problem is the so called “cyber physical production control” (CPPC). It optimally supports the production scheduler in his decision making process based on real-time high-resolution data. With the help of data analytics, the production controller receives decision support over various steps. Due to CPPC, the overall goal of a high adherence to delivery dates can be fundamentally increased.
Understanding the Organizational Impact of Robotic Process Automation: A Socio-Technical Perspective
(2022)
Interest in AI-driven automation software is growing constantly across
all industries, as these technologies enable companies to almost automate administrative processes completely and significantly increase operational efficiency.
However, many implementation attempts fail due to a lack of understanding of how these technologies affect the various socio-technical aspects that are intertwined in an organisation. This leads to a widening gap between value propositions of automation software and the ability of companies to exploit them. For long-term
success, collaboration between humans and software robots in the organization must be optimised. Therefore, the social, technical, and organizational impact of Robotic Process Automation was investigated. Following a socio-technical systems approach, a model was developed and validated in a use case of a company in the mechanical engineering sector. Knowing the influencing factors before launching large-scale automation initiatives will help practitioners to better exploit
efficiency potentials and increase the long-term success.
With big data-technologies on the rise, new fields of application appear in terms of analyzing data to find new relationships for improving process under-standing and stability. Manufacturing companies oftentimes cope with a high number of deviations but struggle to solve them with less effort. The research project BigPro aims to develop a methodology for implementing counter measures to disturbances and deviations derived from big data. This paper proposes a methodology for practitioners to assess predefined counter measures. It consists of a morphology with several criterions that can have a certain characteristic. Those are then combined with a weighting factor to assess the feasibility of the counter measure for prioritization.
Since data becomes more and more important in industrial context, the question arises on how data-driven added value can be measured consistently and comprehensively by manufacturing companies. Currently, attempts on data valuation are primarily taking place on internal company level and qualitative scale. This leads to inconclusive results and unused opportunities in data monetization. Existing approaches in theory to determine quantitative data value are seldom used and less sophisticated. Although quantitative valuation frameworks could enable entities to transfer data valuation from an internal to an external level to take account of progress in digital transformation into external reporting. This paper contributes to data value assessment by presenting a four-part valuation framework that specifies how to transfer internal, qualitative to external, quantitative data valuation. The proposed framework builds on insights derived from practice-oriented action research. The framework is finally tested with a machine tool manufacturer using a single case study approach. Placing value on data will contribute to management’s capability to manage data as well as to realize data-driven benefits and revenue. [https://link.springer.com/chapter/10.1007/978-3-030-85902-2_19]
Viable Production System for adaptable and flexible production planning and control processes
(2009)
High Resolution Supply Chain Management (HRSCM) aims at designing adaptable and flexible production planning and control (PPC) processes according to the needs of the company’s supply chain environment. To reach this goal a model for a Viable Production System (VPS) has been elaborated and is presented in this paper. Based on the Viable System Model (VSM) developed by Stafford Beer current production systems are analyzed in terms of integrity. With the gained knowledge a complete recursive framework of a VPS is developed. The framework allows the design of a decentralized production system that meets all requirements of a dynamic environment. Flexible and adaptable PPC processes can be developed for each identified subsystem of the VPS. Hence, further research focuses on the development of process and control loops in order to assure the application of the framework. Exemplarily the decentralised control loop for inventory management is elaborated in a case study.
This paper addresses the challenge of modelling individual cyber-physical systems (CPS) for small and medium-sized enterprises (SMEs) in manufacturing industries. CPS are key technology building blocks for the implementation of Industrie 4.0. Especially for SMEs the increase of production efficiency and reduction of manufacturing costs through CPS offer potential to maintain their competitiveness and innovation capacity. Although SMEs perceive the potential of CPS, they often lack financial and human resources to acquire the necessary CPS-competencies as well as an overview of all the currently available technological solutions. To overcome this issue a matching platform will offer SMEs support in finding suitable CPS-components by letting them express their functional and technical requirements. The matching logic is based on a set of morphologies that encompasses the functional and requirement spectrum of CPS-components. The matching algorithm analyses the input for congruence of requirements and available technologies and suggests suitable technology combinations. This paper describes the methodology of the matching platform, and introduces the research work to define and to develop the technology morphologies. The presented results facilitate the selection and configuration of CPS for SMEs.
Towards a Methodology to Determine Intersubjective Data Values in Industrial Business Activities
(2021)
This paper contributes to a valuation framework for valuing data as an intangible asset. Especially those industrial manufacturers developing and delivering holistic digital solutions are limited in calculating the true business value of data initiatives. Since the value of data is strongly dependent on the respective use case, a completely objective valuation is not possible. This complicates decision-making on the internal side regarding investments in digital transformation, and on the external side to communicate existing benefits to third parties via financial reporting. Therefore, the target is to design a valuation framework that allows industrial manufacturers to determine an intersubjective, i.e., traceable and transparent, data value. In order to develop a framework that can be applied in practice, the approach is based on industrial case study research.
Organizations, of all sizes, in every domain and in all geographies, are facing growing challenges to comprehend the scope of social media based technologies for their internal process use and for their networks. To assist the CIO’s and executives, FIR has developed a tool based framework to evaluate the impact of social web based collaborative technologies to support knowledge intensive processes. The FSI framework extends organizational spectrum to three categories of Formal, Semi-formal and Informal. The FSI tool places the emphasis on both business process and IT level.
The FSI framework and approach are validated in conjunction with industrial and research clients as test cases. Initial finding, reflected in this article, show a dire mismatch between the process exploitable potential level and organizational ICT profile. At the end, a set of recommendations are included for the organizational management to consider for organizational transformation.
Driven by different trends, such as digitalization, the number of companies aiming for successful business transformation is increasing, while new structures and systems are paving the way. Strategic agile management systems offer significant potential benefits given the increasing speed of the evolving environment in which organizations find themselves these days. To select and implement the appropriate strategic agile management system, companies need to understand the underlying theoretical principles to be able to select the most suitable for the respective company and to introduce it based on individual adaption. Within this paper, a morphology is presented to improve theoretical knowledge about strategic agile management systems. Creating a common understanding of strategic agile management systems and their current areas of application creates a suitable frame of reference for future research projects.
Since 2016, the “Digital in NRW” Competence Centre has been supporting SMEs in the manufacturing industry in designing their individual digital transformation. With an Industry 4.0 maturity assessment, we define the status quo of SMEs, derive SME-specific measures from this, develop a digitalization roadmap and accompany the SME transformation. This paper presents the results of the four-year SME support. By analyzing the results of all maturity assessments, potential analysis and design workshops, we present the most frequent and most effective measures for a successful digital transformation of SMEs. The result of the paper is an action guideline for SMEs to initiate their own digital transformation based on formalized experience.
Augmented reality seems to offer great potential benefits in the field of industrial services. However, the question of the exact benefits, both monetary and qualitative, is difficult to evaluate, as is the case with IT investments in gen-eral. Within the framework of the DM4AR research project, an evaluation model was therefore developed. Based on group discussions and interviews on potential AR use cases, a list of monetary and qualitative benefits was compiled to form the basis for selecting suitable evaluation modules in the existing literature. These include an impact chain analysis in the form of a strategy map, a monetary eval-uation as a calculation of the return on investment, based on the assumptions of the use case as well as existing studies, and a qualitative evaluation in the form of a utility analysis. The outcome is an evaluation model in the form of a multi-perspective approach that considers the impact of AR in the four perspectives of the balanced scorecard (financial, customer, internal business processes, learning and growth). The results of the qualitative and monetary evaluation can be sum-marized in a 2D matrix to support decision-making.
Industrie 4.0 is all around us today: in politics, in the media, and on the agendas of researchers and entrepreneurs. Smarter, faster, more personalized, more efficient, more integrated – those are just some of the promises of this new industrial era. The potential, especially for Germany ́s mechanical
engineering industry and plant engineering sector, is indeed great, both for providers and for users of technologies across the spectrum of Industrie 4.0.
But there are still many unresolved questions, uncertainties, and challenges. Our readiness study seeks to address this need and offer insight. Because Industrie 4.0 will not happen on its own.
This study is intended to bring the grand vision closer to the business reality. We also highlight the challenging milestones that many companies must still pass on the road to Industrie 4.0 readiness.
The study examines where companies in the fields of mechanical and plant engineering currently stand, focusing on what motivates them and what holds them back, and on the differences that emerge between small and medium enterprises on the one hand and large enterprises on the other.
The results make it possible for the first time to develop a detailed, systematic picture of Industrie 4.0 readiness in the engineering sector.
The study concludes with recommendations for action in the business community, complementing the diverse suite of programs and activities offered by VDMA’s Forum Industrie 4.0. We would like to take this opportunity to thank the two sponsors of this project from the VDMA Forum, Dietmar Goericke and Dr. Christian Mosch, whose efforts played a critical role in making this study a success.
We are convinced that Industrie 4.0 can become a success story for Germany’s engineering sector. May our “Industrie 4.0 Readiness” study do its part in this effort.
Towards the Generation of Setup Matrices from Route Sheets and Feedback Data with Data Analytics
(2018)
The function or department of production control in manufacturing companies deals with short-term scheduling of orders and the management of deviations during order execution. Depending on the equipment and characteristics of orders, sequence dependent setup times might occur. In these cases for companies that focus on high utilization of their assets due to long phases of ramp up and high energy costs, it might be optimal to choose sequences with minimal setup time times between orders. Identifying such sequences requires detailed and correct information regarding the specific setup times. With increasing product variety and shorter lot sizes, it becomes more difficult and rather time intense to determine these values manually. One approach is to analyse the relevant features of the orders described in the route sheets or recipes to find similarities in materials and required tools. This paper presents a methodology, which supports setup optimized sequencing for sequence dependent setup times through constructing the setup matrix from such route sheets with the use of data analytics.
Due to Digital Transformation, also called Industry 4.0 or the Industrial Internet of Things, the barrier for implementing data collecting technology on the shop floor has decreased dramatically in the past years – leading to an increasingly growing amount of data from a multitude of IT systems in production companies worldwide. Despite that, the production controller still relies heavily on intrinsic knowledge and intuition for the management of disruptions in production. Thanks to advances in the fields of production control and artificial intelligence, potentials for the collected data for disruption management arise. However, in order to transform data into usable information and allow drawing conclusions for disruption management in production, the relevant data-objects, disturbances and alternative actions must be known. Thus, the decision-making can be supported, reducing the decision latency and increasing benefit of alternative actions. Therefore, the goal of this paper is to discuss the prerequisites necessary to perform a data based disruption management and the methodology itself, serving as an approach to allow companies to build a data basis, classify disruptions and alternative actions in order to improve decision making in the future. [https://link.springer.com/chapter/10.1007/978-3-030-28464-0_13]
High Resolution Supply Chain Management (HRSCM) aims to stop the trend of continuously increasing planning complexity. Today, companies in high-wage countries mostly strive for further optimization of their processes with sophisticated, capital-intensive planning approaches. The capability to adapt flexibly to dynamically changing conditions is limited by the inflexible and centralized planning logic. Thus, flexibility is reached currently by expensive inventory stocks and overcapacities in order to cope with rescheduling of supply or delivery. HRSCM describes the establishment of a complete information transparency in supply chains with the goal of assuring the availability of goods through decentralized, self-optimizing control loops for Production Planning and Control (PPC). HRSCM pursues the idea of enabling organization structures and processes to adapt to dynamic conditions. The approach includes the strengths of the existing planning models as well as the process of decision making in organizations. A precondition for this decentralized adaptation is the synchronization of the objectives of the several units or process owners. The basis for this new PPC Model are information transparency, stable processes, consistent customer orientation, increased capacity flexibility and the understanding of the production system as a viable, socio-technical system.
Digitalization is changing the industrial landscape in a way we did not anticipate. The manufacturing industries worldwide are working to develop strategies and concepts for what is labelled with different terms such as the Industrial Internet of Things in the USA or Industrie 4.0 in Germany. Many industrialized economies are driven by the production sector and this sector needs specific approaches and instruments to take up other than those approaches we know from start-ups and ventures coming from Silicon Valley and other places. In this paper, we demonstrate an appropriate approach to transform producing companies in a systematic and evolutionary approach.
In particular, the objective of this paper is to provide results from two initiatives which conceptually build upon each other and are of particular relevance for the production industry. First, we present a global survey on the state of implementation and the future perspectives of the concept Industrie 4.0 from 2016. Findings from this study have forced parts of the German industry to heavily invest into a common approach to accelerate change towards Industry 4.0 in order to stay competitive in worldwide economy. This approach is presented in a second part.